Merge branch 'dev'

app.py

@@ -6,8 +6,9 @@ import json
 import logging
 import os
 import time
+import cv2
 from contextlib import asynccontextmanager
-from fastapi import FastAPI, WebSocket, HTTPException, Request
+from fastapi import FastAPI, WebSocket, HTTPException
 from fastapi.responses import Response
 
 # Import new modular communication system
@@ -27,8 +28,8 @@ logging.basicConfig(
 logger = logging.getLogger("detector_worker")
 logger.setLevel(logging.DEBUG)
 
-# Store cached frames for REST API access (temporary storage)
-latest_frames = {}
+# Frames are now stored in the shared cache buffer from core.streaming.buffers
+# latest_frames = {}  # Deprecated - using shared_cache_buffer instead
 
 # Lifespan event handler (modern FastAPI approach)
 @asynccontextmanager
@@ -49,7 +50,7 @@ async def lifespan(app: FastAPI):
     worker_state.set_subscriptions([])
     worker_state.session_ids.clear()
     worker_state.progression_stages.clear()
-    latest_frames.clear()
+    # latest_frames.clear()  # No longer needed - frames are in shared_cache_buffer
     logger.info("Detector Worker shutdown complete")
 
 # Create FastAPI application with detailed WebSocket logging
@@ -90,8 +91,8 @@ from core.streaming import initialize_stream_manager
 initialize_stream_manager(max_streams=config.get('max_streams', 10))
 logger.info(f"Initialized stream manager with max_streams={config.get('max_streams', 10)}")
 
-# Store cached frames for REST API access (temporary storage)
-latest_frames = {}
+# Frames are now stored in the shared cache buffer from core.streaming.buffers
+# latest_frames = {}  # Deprecated - using shared_cache_buffer instead
 
 logger.info("Starting detector worker application (refactored)")
 logger.info(f"Configuration: Target FPS: {config.get('target_fps', 10)}, "
@@ -150,31 +151,33 @@ async def get_camera_image(camera_id: str):
                 detail=f"Camera {camera_id} not found or not active"
             )
 
-        # Check if we have a cached frame for this camera
-        if camera_id not in latest_frames:
-            logger.warning(f"No cached frame available for camera '{camera_id}'")
+        # Extract actual camera_id from subscription identifier (displayId;cameraId)
+        # Frames are stored using just the camera_id part
+        actual_camera_id = camera_id.split(';')[-1] if ';' in camera_id else camera_id
+
+        # Get frame from the shared cache buffer
+        from core.streaming.buffers import shared_cache_buffer
+
+        # Only show buffer debug info if camera not found (to reduce log spam)
+        available_cameras = shared_cache_buffer.frame_buffer.get_camera_list()
+
+        frame = shared_cache_buffer.get_frame(actual_camera_id)
+        if frame is None:
+            logger.warning(f"\033[93m[API] No frame for '{actual_camera_id}' - Available: {available_cameras}\033[0m")
             raise HTTPException(
                 status_code=404,
-                detail=f"No frame available for camera {camera_id}"
+                detail=f"No frame available for camera {actual_camera_id}"
             )
 
-        frame = latest_frames[camera_id]
-        logger.debug(f"Retrieved cached frame for camera '{camera_id}', shape: {frame.shape}")
+        # Successful frame retrieval - log only occasionally to avoid spam
 
-        # TODO: This import will be replaced in Phase 3 (Streaming System)
-        # For now, we need to handle the case where OpenCV is not available
-        try:
-            import cv2
-            # Encode frame as JPEG
-            success, buffer_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
-            if not success:
-                raise HTTPException(status_code=500, detail="Failed to encode image as JPEG")
+        # Encode frame as JPEG
+        success, buffer_img = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
+        if not success:
+            raise HTTPException(status_code=500, detail="Failed to encode image as JPEG")
 
-            # Return image as binary response
-            return Response(content=buffer_img.tobytes(), media_type="image/jpeg")
-        except ImportError:
-            logger.error("OpenCV not available for image encoding")
-            raise HTTPException(status_code=500, detail="Image processing not available")
+        # Return image as binary response
+        return Response(content=buffer_img.tobytes(), media_type="image/jpeg")
 
     except HTTPException:
         raise

core/communication/websocket.py

@@ -297,31 +297,31 @@ class WebSocketHandler:
     async def _reconcile_subscriptions_with_tracking(self, target_subscriptions) -> dict:
         """Reconcile subscriptions with tracking integration."""
         try:
-            # First, we need to create tracking integrations for each unique model
+            # Create separate tracking integrations for each subscription (camera isolation)
             tracking_integrations = {}
 
             for subscription_payload in target_subscriptions:
+                subscription_id = subscription_payload['subscriptionIdentifier']
                 model_id = subscription_payload['modelId']
 
-                # Create tracking integration if not already created
-                if model_id not in tracking_integrations:
-                    # Get pipeline configuration for this model
-                    pipeline_parser = model_manager.get_pipeline_config(model_id)
-                    if pipeline_parser:
-                        # Create tracking integration with message sender
-                        tracking_integration = TrackingPipelineIntegration(
-                            pipeline_parser, model_manager, model_id, self._send_message
-                        )
+                # Create separate tracking integration per subscription for camera isolation
+                # Get pipeline configuration for this model
+                pipeline_parser = model_manager.get_pipeline_config(model_id)
+                if pipeline_parser:
+                    # Create tracking integration with message sender (separate instance per camera)
+                    tracking_integration = TrackingPipelineIntegration(
+                        pipeline_parser, model_manager, model_id, self._send_message
+                    )
 
-                        # Initialize tracking model
-                        success = await tracking_integration.initialize_tracking_model()
-                        if success:
-                            tracking_integrations[model_id] = tracking_integration
-                            logger.info(f"[Tracking] Created tracking integration for model {model_id}")
-                        else:
-                            logger.warning(f"[Tracking] Failed to initialize tracking for model {model_id}")
+                    # Initialize tracking model
+                    success = await tracking_integration.initialize_tracking_model()
+                    if success:
+                        tracking_integrations[subscription_id] = tracking_integration
+                        logger.info(f"[Tracking] Created isolated tracking integration for subscription {subscription_id} (model {model_id})")
                     else:
-                        logger.warning(f"[Tracking] No pipeline config found for model {model_id}")
+                        logger.warning(f"[Tracking] Failed to initialize tracking for subscription {subscription_id} (model {model_id})")
+                else:
+                    logger.warning(f"[Tracking] No pipeline config found for model {model_id} in subscription {subscription_id}")
 
             # Now reconcile with StreamManager, adding tracking integrations
             current_subscription_ids = set()
@@ -377,8 +377,10 @@ class WebSocketHandler:
             camera_id = subscription_id.split(';')[-1]
             model_id = payload['modelId']
 
-            # Get tracking integration for this model
-            tracking_integration = tracking_integrations.get(model_id)
+            logger.info(f"[SUBSCRIPTION_MAPPING] subscription_id='{subscription_id}' → camera_id='{camera_id}'")
+
+            # Get tracking integration for this subscription (camera-isolated)
+            tracking_integration = tracking_integrations.get(subscription_id)
 
             # Extract crop coordinates if present
             crop_coords = None
@@ -410,7 +412,7 @@ class WebSocketHandler:
             )
 
             if success and tracking_integration:
-                logger.info(f"[Tracking] Subscription {subscription_id} configured with tracking for model {model_id}")
+                logger.info(f"[Tracking] Subscription {subscription_id} configured with isolated tracking for model {model_id}")
 
             return success
 
@@ -547,10 +549,6 @@ class WebSocketHandler:
         # Update tracking integrations with session ID
         shared_stream_manager.set_session_id(display_identifier, session_id)
 
-        # Save snapshot image after getting sessionId
-        if session_id:
-            await self._save_snapshot(display_identifier, session_id)
-
     async def _handle_set_progression_stage(self, message: SetProgressionStageMessage) -> None:
         """Handle setProgressionStage message."""
         display_identifier = message.payload.displayIdentifier
@@ -566,6 +564,10 @@ class WebSocketHandler:
         if session_id:
             shared_stream_manager.set_progression_stage(session_id, stage)
 
+        # Save snapshot image when progression stage is car_fueling
+        if stage == 'car_fueling' and session_id:
+            await self._save_snapshot(display_identifier, session_id)
+
         # If stage indicates session is cleared/finished, clear from tracking
         if stage in ['finished', 'cleared', 'idle']:
             # Get session ID for this display and clear it

core/models/inference.py

@@ -60,6 +60,8 @@ class YOLOWrapper:
 
         self.model = None
         self._class_names = []
+
+
         self._load_model()
 
         logger.info(f"Initialized YOLO wrapper for {model_id} on {self.device}")
@@ -115,6 +117,7 @@ class YOLOWrapper:
             logger.error(f"Failed to extract class names: {str(e)}")
             self._class_names = {}
 
+
     def infer(
         self,
         image: np.ndarray,
@@ -222,55 +225,30 @@ class YOLOWrapper:
 
         return detections
 
+
     def track(
         self,
         image: np.ndarray,
         confidence_threshold: float = 0.5,
         trigger_classes: Optional[List[str]] = None,
-        persist: bool = True
+        persist: bool = True,
+        camera_id: Optional[str] = None
     ) -> InferenceResult:
         """
-        Run tracking on an image
+        Run detection (tracking will be handled by external tracker)
 
         Args:
             image: Input image as numpy array (BGR format)
             confidence_threshold: Minimum confidence for detections
             trigger_classes: List of class names to filter
-            persist: Whether to persist tracks across frames
+            persist: Ignored - tracking handled externally
+            camera_id: Ignored - tracking handled externally
 
         Returns:
-            InferenceResult containing detections with track IDs
+            InferenceResult containing detections (no track IDs from YOLO)
         """
-        if self.model is None:
-            raise RuntimeError(f"Model {self.model_id} not loaded")
-
-        try:
-            import time
-            start_time = time.time()
-
-            # Run tracking
-            results = self.model.track(
-                image,
-                conf=confidence_threshold,
-                persist=persist,
-                verbose=False
-            )
-
-            inference_time = time.time() - start_time
-
-            # Parse results
-            detections = self._parse_results(results[0], trigger_classes)
-
-            return InferenceResult(
-                detections=detections,
-                image_shape=(image.shape[0], image.shape[1]),
-                inference_time=inference_time,
-                model_id=self.model_id
-            )
-
-        except Exception as e:
-            logger.error(f"Tracking failed for model {self.model_id}: {str(e)}", exc_info=True)
-            raise
+        # Just do detection - no YOLO tracking
+        return self.infer(image, confidence_threshold, trigger_classes)
 
     def predict_classification(
         self,
@@ -350,6 +328,7 @@ class YOLOWrapper:
         """Get the number of classes the model can detect"""
         return len(self._class_names)
 
+
     def clear_cache(self) -> None:
         """Clear the model cache"""
         with self._cache_lock:

core/streaming/buffers.py

@@ -46,13 +46,7 @@ class FrameBuffer:
 
             frame_data = self._frames[camera_id]
 
-            # Check if frame is too old
-            age = time.time() - frame_data['timestamp']
-            if age > self.max_age_seconds:
-                logger.debug(f"Frame for camera {camera_id} is {age:.1f}s old, discarding")
-                del self._frames[camera_id]
-                return None
-
+            # Return frame regardless of age - frames persist until replaced
             return frame_data['frame'].copy()
 
     def get_frame_info(self, camera_id: str) -> Optional[Dict[str, Any]]:
@@ -64,10 +58,7 @@ class FrameBuffer:
             frame_data = self._frames[camera_id]
             age = time.time() - frame_data['timestamp']
 
-            if age > self.max_age_seconds:
-                del self._frames[camera_id]
-                return None
-
+            # Return frame info regardless of age - frames persist until replaced
             return {
                 'timestamp': frame_data['timestamp'],
                 'age': age,
@@ -95,24 +86,10 @@ class FrameBuffer:
             logger.debug(f"Cleared all frames ({count} cameras)")
 
     def get_camera_list(self) -> list:
-        """Get list of cameras with valid frames."""
+        """Get list of cameras with frames - all frames persist until replaced."""
         with self._lock:
-            current_time = time.time()
-            valid_cameras = []
-            expired_cameras = []
-
-            for camera_id, frame_data in self._frames.items():
-                age = current_time - frame_data['timestamp']
-                if age <= self.max_age_seconds:
-                    valid_cameras.append(camera_id)
-                else:
-                    expired_cameras.append(camera_id)
-
-            # Clean up expired frames
-            for camera_id in expired_cameras:
-                del self._frames[camera_id]
-
-            return valid_cameras
+            # Return all cameras that have frames - no age-based filtering
+            return list(self._frames.keys())
 
     def get_stats(self) -> Dict[str, Any]:
         """Get buffer statistics."""
@@ -120,8 +97,8 @@ class FrameBuffer:
             current_time = time.time()
             stats = {
                 'total_cameras': len(self._frames),
-                'valid_cameras': 0,
-                'expired_cameras': 0,
+                'recent_cameras': 0,
+                'stale_cameras': 0,
                 'total_memory_mb': 0,
                 'cameras': {}
             }
@@ -130,16 +107,17 @@ class FrameBuffer:
                 age = current_time - frame_data['timestamp']
                 size_mb = frame_data.get('size_mb', 0)
 
+                # All frames are valid/available, but categorize by freshness for monitoring
                 if age <= self.max_age_seconds:
-                    stats['valid_cameras'] += 1
+                    stats['recent_cameras'] += 1
                 else:
-                    stats['expired_cameras'] += 1
+                    stats['stale_cameras'] += 1
 
                 stats['total_memory_mb'] += size_mb
 
                 stats['cameras'][camera_id] = {
                     'age': age,
-                    'valid': age <= self.max_age_seconds,
+                    'recent': age <= self.max_age_seconds,  # Recent but all frames available
                     'shape': frame_data['shape'],
                     'dtype': frame_data['dtype'],
                     'size_mb': size_mb

core/streaming/manager.py

@@ -130,6 +130,7 @@ class StreamManager:
         try:
             if stream_config.rtsp_url:
                 # RTSP stream using FFmpeg subprocess with CUDA acceleration
+                logger.info(f"\033[94m[RTSP] Starting {camera_id}\033[0m")
                 reader = FFmpegRTSPReader(
                     camera_id=camera_id,
                     rtsp_url=stream_config.rtsp_url,
@@ -138,10 +139,11 @@ class StreamManager:
                 reader.set_frame_callback(self._frame_callback)
                 reader.start()
                 self._streams[camera_id] = reader
-                logger.info(f"Started FFmpeg RTSP stream for camera {camera_id}")
+                logger.info(f"\033[92m[RTSP] {camera_id} connected\033[0m")
 
             elif stream_config.snapshot_url:
                 # HTTP snapshot stream
+                logger.info(f"\033[95m[HTTP] Starting {camera_id}\033[0m")
                 reader = HTTPSnapshotReader(
                     camera_id=camera_id,
                     snapshot_url=stream_config.snapshot_url,
@@ -151,7 +153,7 @@ class StreamManager:
                 reader.set_frame_callback(self._frame_callback)
                 reader.start()
                 self._streams[camera_id] = reader
-                logger.info(f"Started HTTP snapshot stream for camera {camera_id}")
+                logger.info(f"\033[92m[HTTP] {camera_id} connected\033[0m")
 
             else:
                 logger.error(f"No valid URL provided for camera {camera_id}")
@@ -169,8 +171,9 @@ class StreamManager:
             try:
                 self._streams[camera_id].stop()
                 del self._streams[camera_id]
-                shared_cache_buffer.clear_camera(camera_id)
-                logger.info(f"Stopped stream for camera {camera_id}")
+                # DON'T clear frames - they should persist until replaced
+                # shared_cache_buffer.clear_camera(camera_id)  # REMOVED - frames should persist
+                logger.info(f"Stopped stream for camera {camera_id} (frames preserved in buffer)")
             except Exception as e:
                 logger.error(f"Error stopping stream for camera {camera_id}: {e}")
 
@@ -179,6 +182,16 @@ class StreamManager:
         try:
             # Store frame in shared buffer
             shared_cache_buffer.put_frame(camera_id, frame)
+            # Quieter frame callback logging - only log occasionally
+            if hasattr(self, '_frame_log_count'):
+                self._frame_log_count += 1
+            else:
+                self._frame_log_count = 1
+
+            # Log every 100 frames to avoid spam
+            if self._frame_log_count % 100 == 0:
+                available_cameras = shared_cache_buffer.frame_buffer.get_camera_list()
+                logger.info(f"\033[96m[BUFFER] {len(available_cameras)} active cameras: {', '.join(available_cameras)}\033[0m")
 
             # Process tracking for subscriptions with tracking integration
             self._process_tracking_for_camera(camera_id, frame)
@@ -376,20 +389,51 @@ class StreamManager:
                     logger.debug(f"Set session {session_id} for display {display_id}")
 
     def clear_session_id(self, session_id: str):
-        """Clear session ID from tracking integrations."""
+        """Clear session ID from the specific tracking integration handling this session."""
         with self._lock:
+            # Find the subscription that's handling this session
+            session_subscription = None
             for subscription_info in self._subscriptions.values():
                 if subscription_info.tracking_integration:
-                    subscription_info.tracking_integration.clear_session_id(session_id)
-                    logger.debug(f"Cleared session {session_id}")
+                    # Check if this integration is handling the given session_id
+                    integration = subscription_info.tracking_integration
+                    if session_id in integration.session_vehicles:
+                        session_subscription = subscription_info
+                        break
+
+            if session_subscription and session_subscription.tracking_integration:
+                session_subscription.tracking_integration.clear_session_id(session_id)
+                logger.debug(f"Cleared session {session_id} from subscription {session_subscription.subscription_id}")
+            else:
+                logger.warning(f"No tracking integration found for session {session_id}, broadcasting to all subscriptions")
+                # Fallback: broadcast to all (original behavior)
+                for subscription_info in self._subscriptions.values():
+                    if subscription_info.tracking_integration:
+                        subscription_info.tracking_integration.clear_session_id(session_id)
 
     def set_progression_stage(self, session_id: str, stage: str):
-        """Set progression stage for tracking integrations."""
+        """Set progression stage for the specific tracking integration handling this session."""
         with self._lock:
+            # Find the subscription that's handling this session
+            session_subscription = None
             for subscription_info in self._subscriptions.values():
                 if subscription_info.tracking_integration:
-                    subscription_info.tracking_integration.set_progression_stage(session_id, stage)
-                    logger.debug(f"Set progression stage for session {session_id}: {stage}")
+                    # Check if this integration is handling the given session_id
+                    # We need to check the integration's active sessions
+                    integration = subscription_info.tracking_integration
+                    if session_id in integration.session_vehicles:
+                        session_subscription = subscription_info
+                        break
+
+            if session_subscription and session_subscription.tracking_integration:
+                session_subscription.tracking_integration.set_progression_stage(session_id, stage)
+                logger.debug(f"Set progression stage for session {session_id}: {stage} on subscription {session_subscription.subscription_id}")
+            else:
+                logger.warning(f"No tracking integration found for session {session_id}, broadcasting to all subscriptions")
+                # Fallback: broadcast to all (original behavior)
+                for subscription_info in self._subscriptions.values():
+                    if subscription_info.tracking_integration:
+                        subscription_info.tracking_integration.set_progression_stage(session_id, stage)
 
     def get_tracking_stats(self) -> Dict[str, Any]:
         """Get tracking statistics from all subscriptions."""

core/streaming/readers.py

@@ -12,8 +12,7 @@ import os
 import subprocess
 # import fcntl  # No longer needed with atomic file operations
 from typing import Optional, Callable
-from watchdog.observers import Observer
-from watchdog.events import FileSystemEventHandler
+# Removed watchdog imports - no longer using file watching
 
 # Suppress FFMPEG/H.264 error messages if needed
 # Set this environment variable to reduce noise from decoder errors
@@ -22,31 +21,42 @@ os.environ["OPENCV_FFMPEG_LOGLEVEL"] = "-8"  # Suppress FFMPEG warnings
 
 logger = logging.getLogger(__name__)
 
-# Suppress noisy watchdog debug logs
-logging.getLogger('watchdog.observers.inotify_buffer').setLevel(logging.CRITICAL)
-logging.getLogger('watchdog.observers.fsevents').setLevel(logging.CRITICAL)
-logging.getLogger('fsevents').setLevel(logging.CRITICAL)
+# Color codes for pretty logging
+class Colors:
+    GREEN = '\033[92m'
+    YELLOW = '\033[93m'
+    RED = '\033[91m'
+    BLUE = '\033[94m'
+    PURPLE = '\033[95m'
+    CYAN = '\033[96m'
+    WHITE = '\033[97m'
+    BOLD = '\033[1m'
+    END = '\033[0m'
+
+def log_success(camera_id: str, message: str):
+    """Log success messages in green"""
+    logger.info(f"{Colors.GREEN}[{camera_id}] {message}{Colors.END}")
+
+def log_warning(camera_id: str, message: str):
+    """Log warnings in yellow"""
+    logger.warning(f"{Colors.YELLOW}[{camera_id}] {message}{Colors.END}")
+
+def log_error(camera_id: str, message: str):
+    """Log errors in red"""
+    logger.error(f"{Colors.RED}[{camera_id}] {message}{Colors.END}")
+
+def log_info(camera_id: str, message: str):
+    """Log info in cyan"""
+    logger.info(f"{Colors.CYAN}[{camera_id}] {message}{Colors.END}")
+
+# Removed watchdog logging configuration - no longer using file watching
 
 
-class FrameFileHandler(FileSystemEventHandler):
-    """File system event handler for frame file changes."""
-
-    def __init__(self, callback):
-        self.callback = callback
-        self.last_modified = 0
-
-    def on_modified(self, event):
-        if event.is_directory:
-            return
-        # Debounce rapid file changes
-        current_time = time.time()
-        if current_time - self.last_modified > 0.01:  # 10ms debounce
-            self.last_modified = current_time
-            self.callback()
+# Removed FrameFileHandler - no longer using file watching
 
 
 class FFmpegRTSPReader:
-    """RTSP stream reader using subprocess FFmpeg with CUDA hardware acceleration and file watching."""
+    """RTSP stream reader using subprocess FFmpeg piping frames directly to buffer."""
 
     def __init__(self, camera_id: str, rtsp_url: str, max_retries: int = 3):
         self.camera_id = camera_id
@@ -56,10 +66,8 @@ class FFmpegRTSPReader:
         self.stop_event = threading.Event()
         self.thread = None
         self.frame_callback: Optional[Callable] = None
-        self.observer = None
-        self.frame_ready_event = threading.Event()
 
-        # Stream specs
+        # Expected stream specs (for reference, actual dimensions read from PPM header)
         self.width = 1280
         self.height = 720
 
@@ -76,7 +84,7 @@ class FFmpegRTSPReader:
         self.stop_event.clear()
         self.thread = threading.Thread(target=self._read_frames, daemon=True)
         self.thread.start()
-        logger.info(f"Started FFmpeg reader for camera {self.camera_id}")
+        log_success(self.camera_id, "Stream started")
 
     def stop(self):
         """Stop the FFmpeg subprocess reader."""
@@ -89,171 +97,138 @@ class FFmpegRTSPReader:
                 self.process.kill()
         if self.thread:
             self.thread.join(timeout=5.0)
-        logger.info(f"Stopped FFmpeg reader for camera {self.camera_id}")
+        log_info(self.camera_id, "Stream stopped")
+
+    # Removed _probe_stream_info - BMP headers contain dimensions
 
     def _start_ffmpeg_process(self):
-        """Start FFmpeg subprocess writing timestamped frames for atomic reads."""
-        # Create temp file paths for this camera
-        self.frame_dir = "/tmp/frame"
-        os.makedirs(self.frame_dir, exist_ok=True)
-
-        # Use strftime pattern - FFmpeg writes each frame with unique timestamp
-        # This ensures each file is complete when written
-        camera_id_safe = self.camera_id.replace(' ', '_')
-        self.frame_prefix = f"camera_{camera_id_safe}"
-        # Using strftime pattern with microseconds for unique filenames
-        self.frame_pattern = f"{self.frame_dir}/{self.frame_prefix}_%Y%m%d_%H%M%S_%f.ppm"
-
+        """Start FFmpeg subprocess outputting BMP frames to stdout pipe."""
         cmd = [
             'ffmpeg',
             # DO NOT REMOVE
-            '-hwaccel', 'cuda',
-            '-hwaccel_device', '0',
+            # '-hwaccel', 'cuda',
+            # '-hwaccel_device', '0',
             '-rtsp_transport', 'tcp',
             '-i', self.rtsp_url,
-            '-f', 'image2',
-            '-strftime', '1',  # Enable strftime pattern expansion
-            '-pix_fmt', 'rgb24',  # PPM uses RGB not BGR
-            '-an',  # No audio
-            '-y',  # Overwrite output file
-            self.frame_pattern  # Write timestamped frames
+            '-f', 'image2pipe',  # Output images to pipe
+            '-vcodec', 'bmp',    # BMP format with header containing dimensions
+            # Use native stream resolution and framerate
+            '-an',               # No audio
+            '-'                  # Output to stdout
         ]
 
         try:
-            # Log the FFmpeg command for debugging
-            logger.info(f"Starting FFmpeg for camera {self.camera_id} with command: {' '.join(cmd)}")
-
-            # Start FFmpeg detached - we don't need to communicate with it
+            # Start FFmpeg with stdout pipe to read frames directly
             self.process = subprocess.Popen(
                 cmd,
-                stdout=subprocess.DEVNULL,
-                stderr=subprocess.DEVNULL
+                stdout=subprocess.PIPE,  # Capture stdout for frame data
+                stderr=subprocess.DEVNULL,
+                bufsize=0  # Unbuffered for real-time processing
             )
-            logger.info(f"Started FFmpeg process PID {self.process.pid} for camera {self.camera_id} -> {self.frame_pattern}")
             return True
         except Exception as e:
-            logger.error(f"Failed to start FFmpeg for camera {self.camera_id}: {e}")
+            log_error(self.camera_id, f"FFmpeg startup failed: {e}")
             return False
 
-    def _setup_file_watcher(self):
-        """Setup file system watcher for frame directory."""
-        # Setup file watcher for the frame directory
-        handler = FrameFileHandler(lambda: self._on_file_changed())
-        self.observer = Observer()
-        self.observer.schedule(handler, self.frame_dir, recursive=False)
-        self.observer.start()
-        logger.info(f"Started file watcher for {self.frame_dir} with pattern {self.frame_prefix}*.ppm")
+    def _read_bmp_frame(self, pipe):
+        """Read BMP frame from pipe - BMP header contains dimensions."""
+        try:
+            # Read BMP header (14 bytes file header + 40 bytes info header = 54 bytes minimum)
+            header_data = b''
+            bytes_to_read = 54
 
-    def _on_file_changed(self):
-        """Called when a new frame file is created."""
-        # Signal that a new frame might be available
-        self.frame_ready_event.set()
+            while len(header_data) < bytes_to_read:
+                chunk = pipe.read(bytes_to_read - len(header_data))
+                if not chunk:
+                    return None  # Silent end of stream
+                header_data += chunk
+
+            # Parse BMP header
+            if header_data[:2] != b'BM':
+                return None  # Invalid format, skip frame silently
+
+            # Extract file size from header (bytes 2-5)
+            import struct
+            file_size = struct.unpack('<L', header_data[2:6])[0]
+
+            # Extract width and height from info header (bytes 18-21 and 22-25)
+            width = struct.unpack('<L', header_data[18:22])[0]
+            height = struct.unpack('<L', header_data[22:26])[0]
+
+            # Read remaining file data
+            remaining_size = file_size - 54
+            remaining_data = b''
+
+            while len(remaining_data) < remaining_size:
+                chunk = pipe.read(remaining_size - len(remaining_data))
+                if not chunk:
+                    return None  # Stream ended silently
+                remaining_data += chunk
+
+            # Complete BMP data
+            bmp_data = header_data + remaining_data
+
+            # Use OpenCV to decode BMP directly from memory
+            frame_array = np.frombuffer(bmp_data, dtype=np.uint8)
+            frame = cv2.imdecode(frame_array, cv2.IMREAD_COLOR)
+
+            if frame is None:
+                return None  # Decode failed silently
+
+            return frame
+
+        except Exception:
+            return None  # Error reading frame silently
 
     def _read_frames(self):
-        """Reactively read frames when file changes."""
+        """Read frames directly from FFmpeg stdout pipe."""
         frame_count = 0
         last_log_time = time.time()
-        # Remove unused variable: bytes_per_frame = self.width * self.height * 3
-        restart_check_interval = 10  # Check FFmpeg status every 10 seconds
 
         while not self.stop_event.is_set():
             try:
                 # Start FFmpeg if not running
                 if not self.process or self.process.poll() is not None:
                     if self.process and self.process.poll() is not None:
-                        logger.warning(f"FFmpeg process died for camera {self.camera_id}, restarting...")
+                        log_warning(self.camera_id, "Stream disconnected, reconnecting...")
 
                     if not self._start_ffmpeg_process():
                         time.sleep(5.0)
                         continue
 
-                    # Wait for FFmpeg to start writing frame files
-                    wait_count = 0
-                    while wait_count < 30:
-                        # Check if any frame files exist
-                        import glob
-                        frame_files = glob.glob(f"{self.frame_dir}/{self.frame_prefix}*.ppm")
-                        if frame_files:
-                            logger.info(f"Found {len(frame_files)} initial frame files for {self.camera_id}")
-                            break
-                        time.sleep(1.0)
-                        wait_count += 1
+                # Read frames directly from FFmpeg stdout
+                try:
+                    if self.process and self.process.stdout:
+                        # Read BMP frame data
+                        frame = self._read_bmp_frame(self.process.stdout)
+                        if frame is None:
+                            continue
 
-                    if wait_count >= 30:
-                        logger.error(f"No frame files created after 30s for {self.camera_id}")
-                        logger.error(f"Expected pattern: {self.frame_dir}/{self.frame_prefix}*.ppm")
-                        continue
+                        # Call frame callback
+                        if self.frame_callback:
+                            self.frame_callback(self.camera_id, frame)
 
-                    # Setup file watcher
-                    self._setup_file_watcher()
+                        frame_count += 1
 
-                # Wait for file change event (or timeout for health check)
-                if self.frame_ready_event.wait(timeout=restart_check_interval):
-                    self.frame_ready_event.clear()
+                        # Log progress every 60 seconds (quieter)
+                        current_time = time.time()
+                        if current_time - last_log_time >= 60:
+                            log_success(self.camera_id, f"{frame_count} frames captured ({frame.shape[1]}x{frame.shape[0]})")
+                            last_log_time = current_time
 
-                    # Read latest complete frame file
-                    try:
-                        import glob
-                        # Find all frame files for this camera
-                        frame_files = glob.glob(f"{self.frame_dir}/{self.frame_prefix}*.ppm")
+                except Exception:
+                    # Process might have died, let it restart on next iteration
+                    if self.process:
+                        self.process.terminate()
+                        self.process = None
+                    time.sleep(1.0)
 
-                        if frame_files:
-                            # Sort by filename (which includes timestamp) and get the latest
-                            frame_files.sort()
-                            latest_frame = frame_files[-1]
-
-                            # Read the latest frame (it's complete since FFmpeg wrote it atomically)
-                            frame = cv2.imread(latest_frame)
-
-                            if frame is not None and frame.shape == (self.height, self.width, 3):
-                                # Call frame callback directly
-                                if self.frame_callback:
-                                    self.frame_callback(self.camera_id, frame)
-
-                                frame_count += 1
-
-                                # Log progress
-                                current_time = time.time()
-                                if current_time - last_log_time >= 30:
-                                    logger.info(f"Camera {self.camera_id}: {frame_count} frames processed")
-                                    last_log_time = current_time
-
-                            # Clean up old frame files to prevent disk filling
-                            # Keep only the latest 5 frames
-                            if len(frame_files) > 5:
-                                for old_file in frame_files[:-5]:
-                                    try:
-                                        os.remove(old_file)
-                                    except:
-                                        pass
-
-                    except Exception as e:
-                        logger.debug(f"Camera {self.camera_id}: Error reading frames: {e}")
-                        pass
-
-            except Exception as e:
-                logger.error(f"Camera {self.camera_id}: Error in reactive frame reading: {e}")
+            except Exception:
                 time.sleep(1.0)
 
         # Cleanup
-        if self.observer:
-            self.observer.stop()
-            self.observer.join()
         if self.process:
             self.process.terminate()
-        # Clean up all frame files for this camera
-        try:
-            if hasattr(self, 'frame_prefix') and hasattr(self, 'frame_dir'):
-                import glob
-                frame_files = glob.glob(f"{self.frame_dir}/{self.frame_prefix}*.ppm")
-                for frame_file in frame_files:
-                    try:
-                        os.remove(frame_file)
-                    except:
-                        pass
-        except:
-            pass
-        logger.info(f"Reactive FFmpeg reader ended for camera {self.camera_id}")
 
 
 logger = logging.getLogger(__name__)

core/tracking/bot_sort_tracker.py

@@ -0,0 +1,408 @@
+"""
+BoT-SORT Multi-Object Tracker with Camera Isolation
+Based on BoT-SORT: Robust Associations Multi-Pedestrian Tracking
+"""
+
+import logging
+import time
+import numpy as np
+from typing import Dict, List, Optional, Tuple, Any
+from dataclasses import dataclass
+from scipy.optimize import linear_sum_assignment
+from filterpy.kalman import KalmanFilter
+import cv2
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class TrackState:
+    """Track state enumeration"""
+    TENTATIVE = "tentative"      # New track, not confirmed yet
+    CONFIRMED = "confirmed"      # Confirmed track
+    DELETED = "deleted"          # Track to be deleted
+
+
+class Track:
+    """
+    Individual track representation with Kalman filter for motion prediction
+    """
+
+    def __init__(self, detection, track_id: int, camera_id: str):
+        """
+        Initialize a new track
+
+        Args:
+            detection: Initial detection (bbox, confidence, class)
+            track_id: Unique track identifier within camera
+            camera_id: Camera identifier
+        """
+        self.track_id = track_id
+        self.camera_id = camera_id
+        self.state = TrackState.TENTATIVE
+
+        # Time tracking
+        self.start_time = time.time()
+        self.last_update_time = time.time()
+
+        # Appearance and motion
+        self.bbox = detection.bbox  # [x1, y1, x2, y2]
+        self.confidence = detection.confidence
+        self.class_name = detection.class_name
+
+        # Track management
+        self.hit_streak = 1
+        self.time_since_update = 0
+        self.age = 1
+
+        # Kalman filter for motion prediction
+        self.kf = self._create_kalman_filter()
+        self._update_kalman_filter(detection.bbox)
+
+        # Track history
+        self.history = [detection.bbox]
+        self.max_history = 10
+
+    def _create_kalman_filter(self) -> KalmanFilter:
+        """Create Kalman filter for bbox tracking (x, y, w, h, vx, vy, vw, vh)"""
+        kf = KalmanFilter(dim_x=8, dim_z=4)
+
+        # State transition matrix (constant velocity model)
+        kf.F = np.array([
+            [1, 0, 0, 0, 1, 0, 0, 0],
+            [0, 1, 0, 0, 0, 1, 0, 0],
+            [0, 0, 1, 0, 0, 0, 1, 0],
+            [0, 0, 0, 1, 0, 0, 0, 1],
+            [0, 0, 0, 0, 1, 0, 0, 0],
+            [0, 0, 0, 0, 0, 1, 0, 0],
+            [0, 0, 0, 0, 0, 0, 1, 0],
+            [0, 0, 0, 0, 0, 0, 0, 1]
+        ])
+
+        # Measurement matrix (observe x, y, w, h)
+        kf.H = np.array([
+            [1, 0, 0, 0, 0, 0, 0, 0],
+            [0, 1, 0, 0, 0, 0, 0, 0],
+            [0, 0, 1, 0, 0, 0, 0, 0],
+            [0, 0, 0, 1, 0, 0, 0, 0]
+        ])
+
+        # Process noise
+        kf.Q *= 0.01
+
+        # Measurement noise
+        kf.R *= 10
+
+        # Initial covariance
+        kf.P *= 100
+
+        return kf
+
+    def _update_kalman_filter(self, bbox: List[float]):
+        """Update Kalman filter with new bbox"""
+        # Convert [x1, y1, x2, y2] to [cx, cy, w, h]
+        x1, y1, x2, y2 = bbox
+        cx = (x1 + x2) / 2
+        cy = (y1 + y2) / 2
+        w = x2 - x1
+        h = y2 - y1
+
+        # Properly assign to column vector
+        self.kf.x[:4, 0] = [cx, cy, w, h]
+
+    def predict(self) -> np.ndarray:
+        """Predict next position using Kalman filter"""
+        self.kf.predict()
+
+        # Convert back to [x1, y1, x2, y2] format
+        cx, cy, w, h = self.kf.x[:4, 0]  # Extract from column vector
+        x1 = cx - w/2
+        y1 = cy - h/2
+        x2 = cx + w/2
+        y2 = cy + h/2
+
+        return np.array([x1, y1, x2, y2])
+
+    def update(self, detection):
+        """Update track with new detection"""
+        self.last_update_time = time.time()
+        self.time_since_update = 0
+        self.hit_streak += 1
+        self.age += 1
+
+        # Update track properties
+        self.bbox = detection.bbox
+        self.confidence = detection.confidence
+
+        # Update Kalman filter
+        x1, y1, x2, y2 = detection.bbox
+        cx = (x1 + x2) / 2
+        cy = (y1 + y2) / 2
+        w = x2 - x1
+        h = y2 - y1
+
+        self.kf.update([cx, cy, w, h])
+
+        # Update history
+        self.history.append(detection.bbox)
+        if len(self.history) > self.max_history:
+            self.history.pop(0)
+
+        # Update state
+        if self.state == TrackState.TENTATIVE and self.hit_streak >= 3:
+            self.state = TrackState.CONFIRMED
+
+    def mark_missed(self):
+        """Mark track as missed in this frame"""
+        self.time_since_update += 1
+        self.age += 1
+
+        if self.time_since_update > 5:  # Delete after 5 missed frames
+            self.state = TrackState.DELETED
+
+    def is_confirmed(self) -> bool:
+        """Check if track is confirmed"""
+        return self.state == TrackState.CONFIRMED
+
+    def is_deleted(self) -> bool:
+        """Check if track should be deleted"""
+        return self.state == TrackState.DELETED
+
+
+class CameraTracker:
+    """
+    BoT-SORT tracker for a single camera
+    """
+
+    def __init__(self, camera_id: str, max_disappeared: int = 10):
+        """
+        Initialize camera tracker
+
+        Args:
+            camera_id: Unique camera identifier
+            max_disappeared: Maximum frames a track can be missed before deletion
+        """
+        self.camera_id = camera_id
+        self.max_disappeared = max_disappeared
+
+        # Track management
+        self.tracks: Dict[int, Track] = {}
+        self.next_id = 1
+        self.frame_count = 0
+
+        logger.info(f"Initialized BoT-SORT tracker for camera {camera_id}")
+
+    def update(self, detections: List) -> List[Track]:
+        """
+        Update tracker with new detections
+
+        Args:
+            detections: List of Detection objects
+
+        Returns:
+            List of active confirmed tracks
+        """
+        self.frame_count += 1
+
+        # Predict all existing tracks
+        for track in self.tracks.values():
+            track.predict()
+
+        # Associate detections to tracks
+        matched_tracks, unmatched_detections, unmatched_tracks = self._associate(detections)
+
+        # Update matched tracks
+        for track_id, detection in matched_tracks:
+            self.tracks[track_id].update(detection)
+
+        # Mark unmatched tracks as missed
+        for track_id in unmatched_tracks:
+            self.tracks[track_id].mark_missed()
+
+        # Create new tracks for unmatched detections
+        for detection in unmatched_detections:
+            track = Track(detection, self.next_id, self.camera_id)
+            self.tracks[self.next_id] = track
+            self.next_id += 1
+
+        # Remove deleted tracks
+        tracks_to_remove = [tid for tid, track in self.tracks.items() if track.is_deleted()]
+        for tid in tracks_to_remove:
+            del self.tracks[tid]
+
+        # Return confirmed tracks
+        confirmed_tracks = [track for track in self.tracks.values() if track.is_confirmed()]
+
+        return confirmed_tracks
+
+    def _associate(self, detections: List) -> Tuple[List[Tuple[int, Any]], List[Any], List[int]]:
+        """
+        Associate detections to existing tracks using IoU distance
+
+        Returns:
+            (matched_tracks, unmatched_detections, unmatched_tracks)
+        """
+        if not detections or not self.tracks:
+            return [], detections, list(self.tracks.keys())
+
+        # Calculate IoU distance matrix
+        track_ids = list(self.tracks.keys())
+        cost_matrix = np.zeros((len(track_ids), len(detections)))
+
+        for i, track_id in enumerate(track_ids):
+            track = self.tracks[track_id]
+            predicted_bbox = track.predict()
+
+            for j, detection in enumerate(detections):
+                iou = self._calculate_iou(predicted_bbox, detection.bbox)
+                cost_matrix[i, j] = 1 - iou  # Convert IoU to distance
+
+        # Solve assignment problem
+        row_indices, col_indices = linear_sum_assignment(cost_matrix)
+
+        # Filter matches by IoU threshold
+        iou_threshold = 0.3
+        matched_tracks = []
+        matched_detection_indices = set()
+        matched_track_indices = set()
+
+        for row, col in zip(row_indices, col_indices):
+            if cost_matrix[row, col] <= (1 - iou_threshold):
+                track_id = track_ids[row]
+                detection = detections[col]
+                matched_tracks.append((track_id, detection))
+                matched_detection_indices.add(col)
+                matched_track_indices.add(row)
+
+        # Find unmatched detections and tracks
+        unmatched_detections = [detections[i] for i in range(len(detections))
+                             if i not in matched_detection_indices]
+        unmatched_tracks = [track_ids[i] for i in range(len(track_ids))
+                          if i not in matched_track_indices]
+
+        return matched_tracks, unmatched_detections, unmatched_tracks
+
+    def _calculate_iou(self, bbox1: np.ndarray, bbox2: List[float]) -> float:
+        """Calculate IoU between two bounding boxes"""
+        x1_1, y1_1, x2_1, y2_1 = bbox1
+        x1_2, y1_2, x2_2, y2_2 = bbox2
+
+        # Calculate intersection area
+        x1_i = max(x1_1, x1_2)
+        y1_i = max(y1_1, y1_2)
+        x2_i = min(x2_1, x2_2)
+        y2_i = min(y2_1, y2_2)
+
+        if x2_i <= x1_i or y2_i <= y1_i:
+            return 0.0
+
+        intersection = (x2_i - x1_i) * (y2_i - y1_i)
+
+        # Calculate union area
+        area1 = (x2_1 - x1_1) * (y2_1 - y1_1)
+        area2 = (x2_2 - x1_2) * (y2_2 - y1_2)
+        union = area1 + area2 - intersection
+
+        return intersection / union if union > 0 else 0.0
+
+
+class MultiCameraBoTSORT:
+    """
+    Multi-camera BoT-SORT tracker with complete camera isolation
+    """
+
+    def __init__(self, trigger_classes: List[str], min_confidence: float = 0.6):
+        """
+        Initialize multi-camera tracker
+
+        Args:
+            trigger_classes: List of class names to track
+            min_confidence: Minimum detection confidence threshold
+        """
+        self.trigger_classes = trigger_classes
+        self.min_confidence = min_confidence
+
+        # Camera-specific trackers
+        self.camera_trackers: Dict[str, CameraTracker] = {}
+
+        logger.info(f"Initialized MultiCameraBoTSORT with classes={trigger_classes}, "
+                   f"min_confidence={min_confidence}")
+
+    def get_or_create_tracker(self, camera_id: str) -> CameraTracker:
+        """Get or create tracker for specific camera"""
+        if camera_id not in self.camera_trackers:
+            self.camera_trackers[camera_id] = CameraTracker(camera_id)
+            logger.info(f"Created new tracker for camera {camera_id}")
+
+        return self.camera_trackers[camera_id]
+
+    def update(self, camera_id: str, inference_result) -> List[Dict]:
+        """
+        Update tracker for specific camera with detections
+
+        Args:
+            camera_id: Camera identifier
+            inference_result: InferenceResult with detections
+
+        Returns:
+            List of track information dictionaries
+        """
+        # Filter detections by confidence and trigger classes
+        filtered_detections = []
+
+        if hasattr(inference_result, 'detections') and inference_result.detections:
+            for detection in inference_result.detections:
+                if (detection.confidence >= self.min_confidence and
+                    detection.class_name in self.trigger_classes):
+                    filtered_detections.append(detection)
+
+        # Get camera tracker and update
+        tracker = self.get_or_create_tracker(camera_id)
+        confirmed_tracks = tracker.update(filtered_detections)
+
+        # Convert tracks to output format
+        track_results = []
+        for track in confirmed_tracks:
+            track_results.append({
+                'track_id': track.track_id,
+                'camera_id': track.camera_id,
+                'bbox': track.bbox,
+                'confidence': track.confidence,
+                'class_name': track.class_name,
+                'hit_streak': track.hit_streak,
+                'age': track.age
+            })
+
+        return track_results
+
+    def get_statistics(self) -> Dict[str, Any]:
+        """Get tracking statistics across all cameras"""
+        stats = {}
+        total_tracks = 0
+
+        for camera_id, tracker in self.camera_trackers.items():
+            camera_stats = {
+                'active_tracks': len([t for t in tracker.tracks.values() if t.is_confirmed()]),
+                'total_tracks': len(tracker.tracks),
+                'frame_count': tracker.frame_count
+            }
+            stats[camera_id] = camera_stats
+            total_tracks += camera_stats['active_tracks']
+
+        stats['summary'] = {
+            'total_cameras': len(self.camera_trackers),
+            'total_active_tracks': total_tracks
+        }
+
+        return stats
+
+    def reset_camera(self, camera_id: str):
+        """Reset tracking for specific camera"""
+        if camera_id in self.camera_trackers:
+            del self.camera_trackers[camera_id]
+            logger.info(f"Reset tracking for camera {camera_id}")
+
+    def reset_all(self):
+        """Reset all camera trackers"""
+        self.camera_trackers.clear()
+        logger.info("Reset all camera trackers")

core/tracking/integration.py

@@ -63,7 +63,7 @@ class TrackingPipelineIntegration:
         self.pending_processing_data: Dict[str, Dict] = {}  # display_id -> processing data (waiting for session ID)
 
         # Additional validators for enhanced flow control
-        self.permanently_processed: Dict[int, float] = {}  # track_id -> process_time (never process again)
+        self.permanently_processed: Dict[str, float] = {}  # "camera_id:track_id" -> process_time (never process again)
         self.progression_stages: Dict[str, str] = {}  # session_id -> current_stage
         self.last_detection_time: Dict[str, float] = {}  # display_id -> last_detection_timestamp
         self.abandonment_timeout = 3.0  # seconds to wait before declaring car abandoned
@@ -183,7 +183,7 @@ class TrackingPipelineIntegration:
 
             # Run tracking model
             if self.tracking_model:
-                # Run inference with tracking
+                # Run detection-only (tracking handled by our own tracker)
                 tracking_results = self.tracking_model.track(
                     frame,
                     confidence_threshold=self.tracker.min_confidence,
@@ -486,7 +486,10 @@ class TrackingPipelineIntegration:
             self.session_vehicles[session_id] = track_id
 
             # Mark vehicle as permanently processed (won't process again even after session clear)
-            self.permanently_processed[track_id] = time.time()
+            # Use composite key to distinguish same track IDs across different cameras
+            camera_id = display_id  # Using display_id as camera_id for isolation
+            permanent_key = f"{camera_id}:{track_id}"
+            self.permanently_processed[permanent_key] = time.time()
 
             # Remove from pending
             del self.pending_vehicles[display_id]
@@ -667,6 +670,7 @@ class TrackingPipelineIntegration:
         self.executor.shutdown(wait=False)
         self.reset_tracking()
 
+
         # Cleanup detection pipeline
         if self.detection_pipeline:
             self.detection_pipeline.cleanup()

core/tracking/tracker.py

@@ -1,6 +1,6 @@
 """
-Vehicle Tracking Module - Continuous tracking with front_rear_detection model
-Implements vehicle identification, persistence, and motion analysis.
+Vehicle Tracking Module - BoT-SORT based tracking with camera isolation
+Implements vehicle identification, persistence, and motion analysis using external tracker.
 """
 import logging
 import time
@@ -10,6 +10,8 @@ from dataclasses import dataclass, field
 import numpy as np
 from threading import Lock
 
+from .bot_sort_tracker import MultiCameraBoTSORT
+
 logger = logging.getLogger(__name__)
 
 
@@ -17,6 +19,7 @@ logger = logging.getLogger(__name__)
 class TrackedVehicle:
     """Represents a tracked vehicle with all its state information."""
     track_id: int
+    camera_id: str
     first_seen: float
     last_seen: float
     session_id: Optional[str] = None
@@ -30,6 +33,8 @@ class TrackedVehicle:
     processed_pipeline: bool = False
     last_position_history: List[Tuple[float, float]] = field(default_factory=list)
     avg_confidence: float = 0.0
+    hit_streak: int = 0
+    age: int = 0
 
     def update_position(self, bbox: Tuple[int, int, int, int], confidence: float):
         """Update vehicle position and confidence."""
@@ -73,7 +78,7 @@ class TrackedVehicle:
 
 class VehicleTracker:
     """
-    Main vehicle tracking implementation using YOLO tracking capabilities.
+    Main vehicle tracking implementation using BoT-SORT with camera isolation.
     Manages continuous tracking, vehicle identification, and state persistence.
     """
 
@@ -88,18 +93,19 @@ class VehicleTracker:
         self.trigger_classes = self.config.get('trigger_classes', self.config.get('triggerClasses', ['frontal']))
         self.min_confidence = self.config.get('minConfidence', 0.6)
 
-        # Tracking state
-        self.tracked_vehicles: Dict[int, TrackedVehicle] = {}
-        self.next_track_id = 1
+        # BoT-SORT multi-camera tracker
+        self.bot_sort = MultiCameraBoTSORT(self.trigger_classes, self.min_confidence)
+
+        # Tracking state - maintain compatibility with existing code
+        self.tracked_vehicles: Dict[str, Dict[int, TrackedVehicle]] = {}  # camera_id -> {track_id: vehicle}
         self.lock = Lock()
 
         # Tracking parameters
         self.stability_threshold = 0.7
         self.min_stable_frames = 5
-        self.position_tolerance = 50  # pixels
         self.timeout_seconds = 2.0
 
-        logger.info(f"VehicleTracker initialized with trigger_classes={self.trigger_classes}, "
+        logger.info(f"VehicleTracker initialized with BoT-SORT: trigger_classes={self.trigger_classes}, "
                    f"min_confidence={self.min_confidence}")
 
     def process_detections(self,
@@ -107,10 +113,10 @@ class VehicleTracker:
                           display_id: str,
                           frame: np.ndarray) -> List[TrackedVehicle]:
         """
-        Process YOLO detection results and update tracking state.
+        Process detection results using BoT-SORT tracking.
 
         Args:
-            results: YOLO detection results with tracking
+            results: Detection results (InferenceResult)
             display_id: Display identifier for this stream
             frame: Current frame being processed
 
@@ -118,108 +124,67 @@ class VehicleTracker:
             List of currently tracked vehicles
         """
         current_time = time.time()
-        active_tracks = []
+
+        # Extract camera_id from display_id for tracking isolation
+        camera_id = display_id  # Using display_id as camera_id for isolation
 
         with self.lock:
-            # Clean up expired tracks
-            expired_ids = [
-                track_id for track_id, vehicle in self.tracked_vehicles.items()
-                if vehicle.is_expired(self.timeout_seconds)
-            ]
-            for track_id in expired_ids:
-                logger.debug(f"Removing expired track {track_id}")
-                del self.tracked_vehicles[track_id]
+            # Update BoT-SORT tracker
+            track_results = self.bot_sort.update(camera_id, results)
 
-            # Process new detections from InferenceResult
-            if hasattr(results, 'detections') and results.detections:
-                # Process detections from InferenceResult
-                for detection in results.detections:
-                    # Skip if confidence is too low
-                    if detection.confidence < self.min_confidence:
-                        continue
+            # Ensure camera tracking dict exists
+            if camera_id not in self.tracked_vehicles:
+                self.tracked_vehicles[camera_id] = {}
 
-                    # Check if class is in trigger classes
-                    if detection.class_name not in self.trigger_classes:
-                        continue
+            # Update tracked vehicles based on BoT-SORT results
+            current_tracks = {}
+            active_tracks = []
 
-                    # Use track_id if available, otherwise generate one
-                    track_id = detection.track_id if detection.track_id is not None else self.next_track_id
-                    if detection.track_id is None:
-                        self.next_track_id += 1
+            for track_result in track_results:
+                track_id = track_result['track_id']
 
-                    # Get bounding box from Detection object
-                    x1, y1, x2, y2 = detection.bbox
-                    bbox = (int(x1), int(y1), int(x2), int(y2))
+                # Create or update TrackedVehicle
+                if track_id in self.tracked_vehicles[camera_id]:
+                    # Update existing vehicle
+                    vehicle = self.tracked_vehicles[camera_id][track_id]
+                    vehicle.update_position(track_result['bbox'], track_result['confidence'])
+                    vehicle.hit_streak = track_result['hit_streak']
+                    vehicle.age = track_result['age']
 
-                    # Update or create tracked vehicle
-                    confidence = detection.confidence
-                    if track_id in self.tracked_vehicles:
-                        # Update existing track
-                        vehicle = self.tracked_vehicles[track_id]
-                        vehicle.update_position(bbox, confidence)
-                        vehicle.display_id = display_id
+                    # Update stability based on hit_streak
+                    if vehicle.hit_streak >= self.min_stable_frames:
+                        vehicle.is_stable = True
+                        vehicle.stable_frames = vehicle.hit_streak
 
-                        # Check stability
-                        stability = vehicle.calculate_stability()
-                        if stability > self.stability_threshold:
-                            vehicle.stable_frames += 1
-                            if vehicle.stable_frames >= self.min_stable_frames:
-                                vehicle.is_stable = True
-                        else:
-                            vehicle.stable_frames = max(0, vehicle.stable_frames - 1)
-                            if vehicle.stable_frames < self.min_stable_frames:
-                                vehicle.is_stable = False
+                    logger.debug(f"Updated track {track_id}: conf={vehicle.confidence:.2f}, "
+                               f"stable={vehicle.is_stable}, hit_streak={vehicle.hit_streak}")
+                else:
+                    # Create new vehicle
+                    x1, y1, x2, y2 = track_result['bbox']
+                    vehicle = TrackedVehicle(
+                        track_id=track_id,
+                        camera_id=camera_id,
+                        first_seen=current_time,
+                        last_seen=current_time,
+                        display_id=display_id,
+                        confidence=track_result['confidence'],
+                        bbox=tuple(track_result['bbox']),
+                        center=((x1 + x2) / 2, (y1 + y2) / 2),
+                        total_frames=1,
+                        hit_streak=track_result['hit_streak'],
+                        age=track_result['age']
+                    )
+                    vehicle.last_position_history.append(vehicle.center)
+                    logger.info(f"New vehicle tracked: ID={track_id}, camera={camera_id}, display={display_id}")
 
-                        logger.debug(f"Updated track {track_id}: conf={confidence:.2f}, "
-                                   f"stable={vehicle.is_stable}, stability={stability:.2f}")
-                    else:
-                        # Create new track
-                        vehicle = TrackedVehicle(
-                            track_id=track_id,
-                            first_seen=current_time,
-                            last_seen=current_time,
-                            display_id=display_id,
-                            confidence=confidence,
-                            bbox=bbox,
-                            center=((x1 + x2) / 2, (y1 + y2) / 2),
-                            total_frames=1
-                        )
-                        vehicle.last_position_history.append(vehicle.center)
-                        self.tracked_vehicles[track_id] = vehicle
-                        logger.info(f"New vehicle tracked: ID={track_id}, display={display_id}")
+                current_tracks[track_id] = vehicle
+                active_tracks.append(vehicle)
 
-                    active_tracks.append(self.tracked_vehicles[track_id])
+            # Update the camera's tracked vehicles
+            self.tracked_vehicles[camera_id] = current_tracks
 
         return active_tracks
 
-    def _find_closest_track(self, center: Tuple[float, float]) -> Optional[TrackedVehicle]:
-        """
-        Find the closest existing track to a given position.
-
-        Args:
-            center: Center position to match
-
-        Returns:
-            Closest tracked vehicle if within tolerance, None otherwise
-        """
-        min_distance = float('inf')
-        closest_track = None
-
-        for vehicle in self.tracked_vehicles.values():
-            if vehicle.is_expired(0.5):  # Shorter timeout for matching
-                continue
-
-            distance = np.sqrt(
-                (center[0] - vehicle.center[0]) ** 2 +
-                (center[1] - vehicle.center[1]) ** 2
-            )
-
-            if distance < min_distance and distance < self.position_tolerance:
-                min_distance = distance
-                closest_track = vehicle
-
-        return closest_track
-
     def get_stable_vehicles(self, display_id: Optional[str] = None) -> List[TrackedVehicle]:
         """
         Get all stable vehicles, optionally filtered by display.
@@ -231,11 +196,15 @@ class VehicleTracker:
             List of stable tracked vehicles
         """
         with self.lock:
-            stable = [
-                v for v in self.tracked_vehicles.values()
-                if v.is_stable and not v.is_expired(self.timeout_seconds)
-                and (display_id is None or v.display_id == display_id)
-            ]
+            stable = []
+            camera_id = display_id  # Using display_id as camera_id
+
+            if camera_id in self.tracked_vehicles:
+                for vehicle in self.tracked_vehicles[camera_id].values():
+                    if (vehicle.is_stable and not vehicle.is_expired(self.timeout_seconds) and
+                        (display_id is None or vehicle.display_id == display_id)):
+                        stable.append(vehicle)
+
         return stable
 
     def get_vehicle_by_session(self, session_id: str) -> Optional[TrackedVehicle]:
@@ -249,9 +218,11 @@ class VehicleTracker:
             Tracked vehicle if found, None otherwise
         """
         with self.lock:
-            for vehicle in self.tracked_vehicles.values():
-                if vehicle.session_id == session_id:
-                    return vehicle
+            # Search across all cameras
+            for camera_vehicles in self.tracked_vehicles.values():
+                for vehicle in camera_vehicles.values():
+                    if vehicle.session_id == session_id:
+                        return vehicle
         return None
 
     def mark_processed(self, track_id: int, session_id: str):
@@ -263,11 +234,14 @@ class VehicleTracker:
             session_id: Session ID assigned to this vehicle
         """
         with self.lock:
-            if track_id in self.tracked_vehicles:
-                vehicle = self.tracked_vehicles[track_id]
-                vehicle.processed_pipeline = True
-                vehicle.session_id = session_id
-                logger.info(f"Marked vehicle {track_id} as processed with session {session_id}")
+            # Search across all cameras for the track_id
+            for camera_vehicles in self.tracked_vehicles.values():
+                if track_id in camera_vehicles:
+                    vehicle = camera_vehicles[track_id]
+                    vehicle.processed_pipeline = True
+                    vehicle.session_id = session_id
+                    logger.info(f"Marked vehicle {track_id} as processed with session {session_id}")
+                    return
 
     def clear_session(self, session_id: str):
         """
@@ -277,30 +251,43 @@ class VehicleTracker:
             session_id: Session ID to clear
         """
         with self.lock:
-            for vehicle in self.tracked_vehicles.values():
-                if vehicle.session_id == session_id:
-                    logger.info(f"Clearing session {session_id} from vehicle {vehicle.track_id}")
-                    vehicle.session_id = None
-                    # Keep processed_pipeline=True to prevent re-processing
+            # Search across all cameras
+            for camera_vehicles in self.tracked_vehicles.values():
+                for vehicle in camera_vehicles.values():
+                    if vehicle.session_id == session_id:
+                        logger.info(f"Clearing session {session_id} from vehicle {vehicle.track_id}")
+                        vehicle.session_id = None
+                        # Keep processed_pipeline=True to prevent re-processing
 
     def reset_tracking(self):
         """Reset all tracking state."""
         with self.lock:
             self.tracked_vehicles.clear()
-            self.next_track_id = 1
+            self.bot_sort.reset_all()
             logger.info("Vehicle tracking state reset")
 
     def get_statistics(self) -> Dict:
         """Get tracking statistics."""
         with self.lock:
-            total = len(self.tracked_vehicles)
-            stable = sum(1 for v in self.tracked_vehicles.values() if v.is_stable)
-            processed = sum(1 for v in self.tracked_vehicles.values() if v.processed_pipeline)
+            total = 0
+            stable = 0
+            processed = 0
+            all_confidences = []
+
+            # Aggregate stats across all cameras
+            for camera_vehicles in self.tracked_vehicles.values():
+                total += len(camera_vehicles)
+                for vehicle in camera_vehicles.values():
+                    if vehicle.is_stable:
+                        stable += 1
+                    if vehicle.processed_pipeline:
+                        processed += 1
+                    all_confidences.append(vehicle.avg_confidence)
 
             return {
                 'total_tracked': total,
                 'stable_vehicles': stable,
                 'processed_vehicles': processed,
-                'avg_confidence': np.mean([v.avg_confidence for v in self.tracked_vehicles.values()])
-                if self.tracked_vehicles else 0.0
+                'avg_confidence': np.mean(all_confidences) if all_confidences else 0.0,
+                'bot_sort_stats': self.bot_sort.get_statistics()
             }

core/tracking/validator.py

@@ -36,8 +36,14 @@ class ValidationResult:
 
 class StableCarValidator:
     """
-    Validates whether a tracked vehicle is stable (fueling) or just passing by.
-    Uses multiple criteria including position stability, duration, and movement patterns.
+    Validates whether a tracked vehicle should be processed through the pipeline.
+
+    Updated for BoT-SORT integration: Trusts the sophisticated BoT-SORT tracking algorithm
+    for stability determination and focuses on business logic validation:
+    - Duration requirements for processing
+    - Confidence thresholds
+    - Session management and cooldowns
+    - Camera isolation with composite keys
     """
 
     def __init__(self, config: Optional[Dict] = None):
@@ -169,7 +175,10 @@ class StableCarValidator:
 
     def _determine_vehicle_state(self, vehicle: TrackedVehicle) -> VehicleState:
         """
-        Determine the current state of the vehicle based on movement patterns.
+        Determine the current state of the vehicle based on BoT-SORT tracking results.
+
+        BoT-SORT provides sophisticated tracking, so we trust its stability determination
+        and focus on business logic validation.
 
         Args:
             vehicle: The tracked vehicle
@@ -177,53 +186,44 @@ class StableCarValidator:
         Returns:
             Current vehicle state
         """
-        # Not enough data
-        if len(vehicle.last_position_history) < 3:
-            return VehicleState.UNKNOWN
-
-        # Calculate velocity
-        velocity = self._calculate_velocity(vehicle)
-
-        # Get position zones
-        x_position = vehicle.center[0] / self.frame_width
-        y_position = vehicle.center[1] / self.frame_height
-
-        # Check if vehicle is stable
-        stability = vehicle.calculate_stability()
-        if stability > 0.7 and velocity < self.velocity_threshold:
-            # Check if it's been stable long enough
+        # Trust BoT-SORT's stability determination
+        if vehicle.is_stable:
+            # Check if it's been stable long enough for processing
             duration = time.time() - vehicle.first_seen
-            if duration > self.min_stable_duration and vehicle.stable_frames >= self.min_stable_frames:
+            if duration >= self.min_stable_duration:
                 return VehicleState.STABLE
             else:
                 return VehicleState.ENTERING
 
-        # Check if vehicle is entering or leaving
+        # For non-stable vehicles, use simplified state determination
+        if len(vehicle.last_position_history) < 2:
+            return VehicleState.UNKNOWN
+
+        # Calculate velocity for movement classification
+        velocity = self._calculate_velocity(vehicle)
+
+        # Basic movement classification
         if velocity > self.velocity_threshold:
-            # Determine direction based on position history
-            positions = np.array(vehicle.last_position_history)
-            if len(positions) >= 2:
-                direction = positions[-1] - positions[0]
+            # Vehicle is moving - classify as passing by or entering/leaving
+            x_position = vehicle.center[0] / self.frame_width
 
-                # Entering: moving towards center
-                if x_position < self.entering_zone_ratio or x_position > (1 - self.entering_zone_ratio):
-                    if abs(direction[0]) > abs(direction[1]):  # Horizontal movement
-                        if (x_position < 0.5 and direction[0] > 0) or (x_position > 0.5 and direction[0] < 0):
-                            return VehicleState.ENTERING
+            # Simple heuristic: vehicles near edges are entering/leaving, center vehicles are passing
+            if x_position < 0.2 or x_position > 0.8:
+                return VehicleState.ENTERING
+            else:
+                return VehicleState.PASSING_BY
 
-                # Leaving: moving away from center
-                if 0.3 < x_position < 0.7:  # In center zone
-                    if abs(direction[0]) > abs(direction[1]):  # Horizontal movement
-                        if abs(direction[0]) > 10:  # Significant movement
-                            return VehicleState.LEAVING
-
-            return VehicleState.PASSING_BY
-
-        return VehicleState.UNKNOWN
+        # Low velocity but not marked stable by tracker - likely entering
+        return VehicleState.ENTERING
 
     def _validate_stable_vehicle(self, vehicle: TrackedVehicle) -> ValidationResult:
         """
-        Perform detailed validation of a stable vehicle.
+        Perform business logic validation of a stable vehicle.
+
+        Since BoT-SORT already determined the vehicle is stable, we focus on:
+        - Duration requirements for processing
+        - Confidence thresholds
+        - Business logic constraints
 
         Args:
             vehicle: The stable vehicle to validate
@@ -231,7 +231,7 @@ class StableCarValidator:
         Returns:
             Detailed validation result
         """
-        # Check duration
+        # Check duration (business requirement)
         duration = time.time() - vehicle.first_seen
         if duration < self.min_stable_duration:
             return ValidationResult(
@@ -243,18 +243,7 @@ class StableCarValidator:
                 track_id=vehicle.track_id
             )
 
-        # Check frame count
-        if vehicle.stable_frames < self.min_stable_frames:
-            return ValidationResult(
-                is_valid=False,
-                state=VehicleState.STABLE,
-                confidence=0.6,
-                reason=f"Not enough stable frames ({vehicle.stable_frames} < {self.min_stable_frames})",
-                should_process=False,
-                track_id=vehicle.track_id
-            )
-
-        # Check confidence
+        # Check confidence (business requirement)
         if vehicle.avg_confidence < self.min_confidence:
             return ValidationResult(
                 is_valid=False,
@@ -265,28 +254,19 @@ class StableCarValidator:
                 track_id=vehicle.track_id
             )
 
-        # Check position variance
-        variance = self._calculate_position_variance(vehicle)
-        if variance > self.position_variance_threshold:
-            return ValidationResult(
-                is_valid=False,
-                state=VehicleState.STABLE,
-                confidence=0.7,
-                reason=f"Position variance too high ({variance:.1f} > {self.position_variance_threshold})",
-                should_process=False,
-                track_id=vehicle.track_id
-            )
+        # Trust BoT-SORT's stability determination - skip position variance check
+        # BoT-SORT's sophisticated tracking already ensures consistent positioning
 
-        # Check state history consistency
+        # Simplified state history check - just ensure recent stability
         if vehicle.track_id in self.validation_history:
-            history = self.validation_history[vehicle.track_id][-5:]  # Last 5 states
+            history = self.validation_history[vehicle.track_id][-3:]  # Last 3 states
             stable_count = sum(1 for s in history if s == VehicleState.STABLE)
-            if stable_count < 3:
+            if len(history) >= 2 and stable_count == 0:  # Only fail if clear instability
                 return ValidationResult(
                     is_valid=False,
                     state=VehicleState.STABLE,
                     confidence=0.7,
-                    reason="Inconsistent state history",
+                    reason="Recent state history shows instability",
                     should_process=False,
                     track_id=vehicle.track_id
                 )
@@ -298,7 +278,7 @@ class StableCarValidator:
             is_valid=True,
             state=VehicleState.STABLE,
             confidence=vehicle.avg_confidence,
-            reason="Vehicle is stable and ready for processing",
+            reason="Vehicle is stable and ready for processing (BoT-SORT validated)",
             should_process=True,
             track_id=vehicle.track_id
         )
@@ -354,25 +334,28 @@ class StableCarValidator:
     def should_skip_same_car(self,
                             vehicle: TrackedVehicle,
                             session_cleared: bool = False,
-                            permanently_processed: Dict[int, float] = None) -> bool:
+                            permanently_processed: Dict[str, float] = None) -> bool:
         """
         Determine if we should skip processing for the same car after session clear.
 
         Args:
             vehicle: The tracked vehicle
             session_cleared: Whether the session was recently cleared
-            permanently_processed: Dict of permanently processed vehicles
+            permanently_processed: Dict of permanently processed vehicles (camera_id:track_id -> time)
 
         Returns:
             True if we should skip this vehicle
         """
         # Check if this vehicle was permanently processed (never process again)
-        if permanently_processed and vehicle.track_id in permanently_processed:
-            process_time = permanently_processed[vehicle.track_id]
-            time_since = time.time() - process_time
-            logger.debug(f"Skipping permanently processed vehicle {vehicle.track_id} "
-                        f"(processed {time_since:.1f}s ago)")
-            return True
+        if permanently_processed:
+            # Create composite key using camera_id and track_id
+            permanent_key = f"{vehicle.camera_id}:{vehicle.track_id}"
+            if permanent_key in permanently_processed:
+                process_time = permanently_processed[permanent_key]
+                time_since = time.time() - process_time
+                logger.debug(f"Skipping permanently processed vehicle {vehicle.track_id} on camera {vehicle.camera_id} "
+                            f"(processed {time_since:.1f}s ago)")
+                return True
 
         # If vehicle has a session_id but it was cleared, skip for a period
         if vehicle.session_id is None and vehicle.processed_pipeline and session_cleared: