feat: update tracking working

core/tracking/integration.py

@@ -146,6 +146,17 @@ class TrackingPipelineIntegration:
                     persist=True
                 )
 
+                # Debug: Log raw detection results
+                if tracking_results and hasattr(tracking_results, 'detections'):
+                    raw_detections = len(tracking_results.detections)
+                    if raw_detections > 0:
+                        class_names = [detection.class_name for detection in tracking_results.detections]
+                        logger.info(f"[DEBUG] Raw detections: {raw_detections}, classes: {class_names}")
+                    else:
+                        logger.debug(f"[DEBUG] No raw detections found")
+                else:
+                    logger.debug(f"[DEBUG] No tracking results or detections attribute")
+
                 # Process tracking results
                 tracked_vehicles = self.tracker.process_detections(
                     tracking_results,

core/tracking/tracker.py

@@ -130,33 +130,29 @@ class VehicleTracker:
                 logger.debug(f"Removing expired track {track_id}")
                 del self.tracked_vehicles[track_id]
 
-            # Process new detections
+            # Process new detections from InferenceResult
-            if hasattr(results, 'boxes') and results.boxes is not None:
+            if hasattr(results, 'detections') and results.detections:
-                boxes = results.boxes
+                # Process detections from InferenceResult
-
+                for detection in results.detections:
-                # Check if tracking is available
+                    # Skip if confidence is too low
-                if hasattr(boxes, 'id') and boxes.id is not None:
+                    if detection.confidence < self.min_confidence:
-                    # Process tracked objects
-                    for i, box in enumerate(boxes):
-                        # Get tracking ID
-                        track_id = int(boxes.id[i].item()) if boxes.id[i] is not None else None
-                        if track_id is None:
                         continue
 
-                        # Get class and confidence
-                        cls_id = int(box.cls.item())
-                        confidence = float(box.conf.item())
-
                     # Check if class is in trigger classes
-                        class_name = results.names[cls_id] if hasattr(results, 'names') else str(cls_id)
+                    if detection.class_name not in self.trigger_classes:
-                        if class_name not in self.trigger_classes and confidence < self.min_confidence:
                         continue
 
-                        # Get bounding box
+                    # Use track_id if available, otherwise generate one
-                        x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
+                    track_id = detection.track_id if detection.track_id is not None else self.next_track_id
-                        bbox = (x1, y1, x2, y2)
+                    if detection.track_id is None:
+                        self.next_track_id += 1
+
+                    # Get bounding box from Detection object
+                    x1, y1, x2, y2 = detection.bbox
+                    bbox = (int(x1), int(y1), int(x2), int(y2))
 
                     # Update or create tracked vehicle
+                    confidence = detection.confidence
                     if track_id in self.tracked_vehicles:
                         # Update existing track
                         vehicle = self.tracked_vehicles[track_id]
@@ -193,48 +189,6 @@ class VehicleTracker:
                         logger.info(f"New vehicle tracked: ID={track_id}, display={display_id}")
 
                     active_tracks.append(self.tracked_vehicles[track_id])
-                else:
-                    # No tracking available, process as detections only
-                    logger.debug("No tracking IDs available, processing as detections only")
-                    for i, box in enumerate(boxes):
-                        cls_id = int(box.cls.item())
-                        confidence = float(box.conf.item())
-
-                        # Check confidence threshold
-                        if confidence < self.min_confidence:
-                            continue
-
-                        # Get bounding box
-                        x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
-                        bbox = (x1, y1, x2, y2)
-                        center = ((x1 + x2) / 2, (y1 + y2) / 2)
-
-                        # Try to match with existing tracks by position
-                        matched_track = self._find_closest_track(center)
-
-                        if matched_track:
-                            matched_track.update_position(bbox, confidence)
-                            matched_track.display_id = display_id
-                            active_tracks.append(matched_track)
-                        else:
-                            # Create new track with generated ID
-                            track_id = self.next_track_id
-                            self.next_track_id += 1
-
-                            vehicle = TrackedVehicle(
-                                track_id=track_id,
-                                first_seen=current_time,
-                                last_seen=current_time,
-                                display_id=display_id,
-                                confidence=confidence,
-                                bbox=bbox,
-                                center=center,
-                                total_frames=1
-                            )
-                            vehicle.last_position_history.append(center)
-                            self.tracked_vehicles[track_id] = vehicle
-                            active_tracks.append(vehicle)
-                            logger.info(f"New vehicle detected (no tracking): ID={track_id}")
 
         return active_tracks