feat: custom bot-sort based tracker
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8 changed files with 649 additions and 282 deletions
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@ -1,6 +1,6 @@
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"""
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Vehicle Tracking Module - Continuous tracking with front_rear_detection model
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Implements vehicle identification, persistence, and motion analysis.
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Vehicle Tracking Module - BoT-SORT based tracking with camera isolation
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Implements vehicle identification, persistence, and motion analysis using external tracker.
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"""
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import logging
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import time
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@ -10,6 +10,8 @@ from dataclasses import dataclass, field
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import numpy as np
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from threading import Lock
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from .bot_sort_tracker import MultiCameraBoTSORT
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logger = logging.getLogger(__name__)
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@ -17,6 +19,7 @@ logger = logging.getLogger(__name__)
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class TrackedVehicle:
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"""Represents a tracked vehicle with all its state information."""
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track_id: int
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camera_id: str
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first_seen: float
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last_seen: float
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session_id: Optional[str] = None
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@ -30,6 +33,8 @@ class TrackedVehicle:
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processed_pipeline: bool = False
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last_position_history: List[Tuple[float, float]] = field(default_factory=list)
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avg_confidence: float = 0.0
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hit_streak: int = 0
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age: int = 0
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def update_position(self, bbox: Tuple[int, int, int, int], confidence: float):
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"""Update vehicle position and confidence."""
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@ -73,7 +78,7 @@ class TrackedVehicle:
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class VehicleTracker:
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"""
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Main vehicle tracking implementation using YOLO tracking capabilities.
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Main vehicle tracking implementation using BoT-SORT with camera isolation.
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Manages continuous tracking, vehicle identification, and state persistence.
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"""
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@ -88,18 +93,19 @@ class VehicleTracker:
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self.trigger_classes = self.config.get('trigger_classes', self.config.get('triggerClasses', ['frontal']))
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self.min_confidence = self.config.get('minConfidence', 0.6)
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# Tracking state
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self.tracked_vehicles: Dict[int, TrackedVehicle] = {}
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self.next_track_id = 1
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# BoT-SORT multi-camera tracker
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self.bot_sort = MultiCameraBoTSORT(self.trigger_classes, self.min_confidence)
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# Tracking state - maintain compatibility with existing code
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self.tracked_vehicles: Dict[str, Dict[int, TrackedVehicle]] = {} # camera_id -> {track_id: vehicle}
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self.lock = Lock()
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# Tracking parameters
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self.stability_threshold = 0.7
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self.min_stable_frames = 5
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self.position_tolerance = 50 # pixels
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self.timeout_seconds = 2.0
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logger.info(f"VehicleTracker initialized with trigger_classes={self.trigger_classes}, "
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logger.info(f"VehicleTracker initialized with BoT-SORT: trigger_classes={self.trigger_classes}, "
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f"min_confidence={self.min_confidence}")
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def process_detections(self,
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@ -107,10 +113,10 @@ class VehicleTracker:
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display_id: str,
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frame: np.ndarray) -> List[TrackedVehicle]:
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"""
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Process YOLO detection results and update tracking state.
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Process detection results using BoT-SORT tracking.
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Args:
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results: YOLO detection results with tracking
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results: Detection results (InferenceResult)
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display_id: Display identifier for this stream
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frame: Current frame being processed
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@ -118,108 +124,67 @@ class VehicleTracker:
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List of currently tracked vehicles
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"""
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current_time = time.time()
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active_tracks = []
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# Extract camera_id from display_id for tracking isolation
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camera_id = display_id # Using display_id as camera_id for isolation
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with self.lock:
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# Clean up expired tracks
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expired_ids = [
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track_id for track_id, vehicle in self.tracked_vehicles.items()
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if vehicle.is_expired(self.timeout_seconds)
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]
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for track_id in expired_ids:
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logger.debug(f"Removing expired track {track_id}")
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del self.tracked_vehicles[track_id]
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# Update BoT-SORT tracker
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track_results = self.bot_sort.update(camera_id, results)
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# Process new detections from InferenceResult
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if hasattr(results, 'detections') and results.detections:
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# Process detections from InferenceResult
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for detection in results.detections:
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# Skip if confidence is too low
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if detection.confidence < self.min_confidence:
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continue
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# Ensure camera tracking dict exists
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if camera_id not in self.tracked_vehicles:
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self.tracked_vehicles[camera_id] = {}
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# Check if class is in trigger classes
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if detection.class_name not in self.trigger_classes:
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continue
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# Update tracked vehicles based on BoT-SORT results
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current_tracks = {}
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active_tracks = []
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# Use track_id if available, otherwise generate one
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track_id = detection.track_id if detection.track_id is not None else self.next_track_id
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if detection.track_id is None:
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self.next_track_id += 1
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for track_result in track_results:
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track_id = track_result['track_id']
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# Get bounding box from Detection object
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x1, y1, x2, y2 = detection.bbox
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bbox = (int(x1), int(y1), int(x2), int(y2))
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# Create or update TrackedVehicle
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if track_id in self.tracked_vehicles[camera_id]:
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# Update existing vehicle
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vehicle = self.tracked_vehicles[camera_id][track_id]
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vehicle.update_position(track_result['bbox'], track_result['confidence'])
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vehicle.hit_streak = track_result['hit_streak']
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vehicle.age = track_result['age']
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# Update or create tracked vehicle
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confidence = detection.confidence
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if track_id in self.tracked_vehicles:
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# Update existing track
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vehicle = self.tracked_vehicles[track_id]
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vehicle.update_position(bbox, confidence)
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vehicle.display_id = display_id
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# Update stability based on hit_streak
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if vehicle.hit_streak >= self.min_stable_frames:
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vehicle.is_stable = True
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vehicle.stable_frames = vehicle.hit_streak
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# Check stability
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stability = vehicle.calculate_stability()
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if stability > self.stability_threshold:
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vehicle.stable_frames += 1
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if vehicle.stable_frames >= self.min_stable_frames:
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vehicle.is_stable = True
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else:
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vehicle.stable_frames = max(0, vehicle.stable_frames - 1)
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if vehicle.stable_frames < self.min_stable_frames:
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vehicle.is_stable = False
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logger.debug(f"Updated track {track_id}: conf={vehicle.confidence:.2f}, "
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f"stable={vehicle.is_stable}, hit_streak={vehicle.hit_streak}")
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else:
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# Create new vehicle
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x1, y1, x2, y2 = track_result['bbox']
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vehicle = TrackedVehicle(
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track_id=track_id,
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camera_id=camera_id,
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first_seen=current_time,
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last_seen=current_time,
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display_id=display_id,
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confidence=track_result['confidence'],
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bbox=tuple(track_result['bbox']),
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center=((x1 + x2) / 2, (y1 + y2) / 2),
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total_frames=1,
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hit_streak=track_result['hit_streak'],
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age=track_result['age']
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)
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vehicle.last_position_history.append(vehicle.center)
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logger.info(f"New vehicle tracked: ID={track_id}, camera={camera_id}, display={display_id}")
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logger.debug(f"Updated track {track_id}: conf={confidence:.2f}, "
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f"stable={vehicle.is_stable}, stability={stability:.2f}")
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else:
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# Create new track
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vehicle = TrackedVehicle(
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track_id=track_id,
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first_seen=current_time,
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last_seen=current_time,
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display_id=display_id,
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confidence=confidence,
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bbox=bbox,
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center=((x1 + x2) / 2, (y1 + y2) / 2),
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total_frames=1
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)
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vehicle.last_position_history.append(vehicle.center)
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self.tracked_vehicles[track_id] = vehicle
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logger.info(f"New vehicle tracked: ID={track_id}, display={display_id}")
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current_tracks[track_id] = vehicle
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active_tracks.append(vehicle)
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active_tracks.append(self.tracked_vehicles[track_id])
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# Update the camera's tracked vehicles
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self.tracked_vehicles[camera_id] = current_tracks
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return active_tracks
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def _find_closest_track(self, center: Tuple[float, float]) -> Optional[TrackedVehicle]:
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"""
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Find the closest existing track to a given position.
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Args:
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center: Center position to match
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Returns:
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Closest tracked vehicle if within tolerance, None otherwise
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"""
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min_distance = float('inf')
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closest_track = None
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for vehicle in self.tracked_vehicles.values():
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if vehicle.is_expired(0.5): # Shorter timeout for matching
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continue
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distance = np.sqrt(
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(center[0] - vehicle.center[0]) ** 2 +
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(center[1] - vehicle.center[1]) ** 2
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)
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if distance < min_distance and distance < self.position_tolerance:
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min_distance = distance
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closest_track = vehicle
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return closest_track
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def get_stable_vehicles(self, display_id: Optional[str] = None) -> List[TrackedVehicle]:
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"""
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Get all stable vehicles, optionally filtered by display.
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@ -231,11 +196,15 @@ class VehicleTracker:
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List of stable tracked vehicles
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"""
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with self.lock:
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stable = [
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v for v in self.tracked_vehicles.values()
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if v.is_stable and not v.is_expired(self.timeout_seconds)
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and (display_id is None or v.display_id == display_id)
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]
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stable = []
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camera_id = display_id # Using display_id as camera_id
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if camera_id in self.tracked_vehicles:
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for vehicle in self.tracked_vehicles[camera_id].values():
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if (vehicle.is_stable and not vehicle.is_expired(self.timeout_seconds) and
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(display_id is None or vehicle.display_id == display_id)):
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stable.append(vehicle)
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return stable
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def get_vehicle_by_session(self, session_id: str) -> Optional[TrackedVehicle]:
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@ -249,9 +218,11 @@ class VehicleTracker:
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Tracked vehicle if found, None otherwise
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"""
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with self.lock:
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for vehicle in self.tracked_vehicles.values():
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if vehicle.session_id == session_id:
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return vehicle
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# Search across all cameras
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for camera_vehicles in self.tracked_vehicles.values():
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for vehicle in camera_vehicles.values():
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if vehicle.session_id == session_id:
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return vehicle
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return None
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def mark_processed(self, track_id: int, session_id: str):
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@ -263,11 +234,14 @@ class VehicleTracker:
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session_id: Session ID assigned to this vehicle
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"""
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with self.lock:
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if track_id in self.tracked_vehicles:
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vehicle = self.tracked_vehicles[track_id]
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vehicle.processed_pipeline = True
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vehicle.session_id = session_id
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logger.info(f"Marked vehicle {track_id} as processed with session {session_id}")
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# Search across all cameras for the track_id
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for camera_vehicles in self.tracked_vehicles.values():
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if track_id in camera_vehicles:
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vehicle = camera_vehicles[track_id]
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vehicle.processed_pipeline = True
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vehicle.session_id = session_id
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logger.info(f"Marked vehicle {track_id} as processed with session {session_id}")
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return
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def clear_session(self, session_id: str):
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"""
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@ -277,30 +251,43 @@ class VehicleTracker:
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session_id: Session ID to clear
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"""
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with self.lock:
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for vehicle in self.tracked_vehicles.values():
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if vehicle.session_id == session_id:
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logger.info(f"Clearing session {session_id} from vehicle {vehicle.track_id}")
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vehicle.session_id = None
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# Keep processed_pipeline=True to prevent re-processing
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# Search across all cameras
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for camera_vehicles in self.tracked_vehicles.values():
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for vehicle in camera_vehicles.values():
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if vehicle.session_id == session_id:
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logger.info(f"Clearing session {session_id} from vehicle {vehicle.track_id}")
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vehicle.session_id = None
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# Keep processed_pipeline=True to prevent re-processing
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def reset_tracking(self):
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"""Reset all tracking state."""
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with self.lock:
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self.tracked_vehicles.clear()
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self.next_track_id = 1
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self.bot_sort.reset_all()
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logger.info("Vehicle tracking state reset")
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def get_statistics(self) -> Dict:
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"""Get tracking statistics."""
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with self.lock:
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total = len(self.tracked_vehicles)
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stable = sum(1 for v in self.tracked_vehicles.values() if v.is_stable)
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processed = sum(1 for v in self.tracked_vehicles.values() if v.processed_pipeline)
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total = 0
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stable = 0
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processed = 0
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all_confidences = []
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# Aggregate stats across all cameras
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for camera_vehicles in self.tracked_vehicles.values():
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total += len(camera_vehicles)
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for vehicle in camera_vehicles.values():
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if vehicle.is_stable:
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stable += 1
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if vehicle.processed_pipeline:
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processed += 1
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all_confidences.append(vehicle.avg_confidence)
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return {
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'total_tracked': total,
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'stable_vehicles': stable,
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'processed_vehicles': processed,
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'avg_confidence': np.mean([v.avg_confidence for v in self.tracked_vehicles.values()])
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if self.tracked_vehicles else 0.0
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'avg_confidence': np.mean(all_confidences) if all_confidences else 0.0,
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'bot_sort_stats': self.bot_sort.get_statistics()
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}
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