refactor(trackers): decouple DeepSORTKalmanBoxTracker from SORTKalmanBoxTracker

trackers/core/deepsort/kalman_box_tracker.py

@@ -11,10 +11,45 @@ class DeepSORTKalmanBoxTracker(SORTKalmanBoxTracker):
     tracked object (bounding box), with a Kalman filter to predict and update
     its position. It also maintains a feature vector for the object, which is
     used to identify the object across frames.
+
+    Attributes:
+        tracker_id (int): Unique identifier for the tracker.
+        number_of_successful_updates (int): Number of times the object has been
+            updated successfully.
+        time_since_update (int): Number of frames since the last update.
+        state (np.ndarray): State vector of the bounding box.
+        F (np.ndarray): State transition matrix.
+        H (np.ndarray): Measurement matrix.
+        Q (np.ndarray): Process noise covariance matrix.
+        R (np.ndarray): Measurement noise covariance matrix.
+        P (np.ndarray): Error covariance matrix.
+        features (list[np.ndarray]): List of feature vectors.
+        count_id (int): Class variable to assign unique IDs to each tracker.
+
+    Args:
+        bbox (np.ndarray): Initial bounding box in the form [x1, y1, x2, y2].
+        feature (Optional[np.ndarray]): Optional initial feature vector.
     """
 
+    count_id = 0
+
+    @classmethod
+    def get_next_tracker_id(cls) -> int:
+        """
+        Class method that returns the next available tracker ID.
+
+        Returns:
+            int: The next available tracker ID.
+        """
+        next_id = cls.count_id
+        cls.count_id += 1
+        return next_id
+
     def __init__(self, bbox: np.ndarray, feature: Optional[np.ndarray] = None):
+        # Call the parent class constructor to handle the basic tracker functionality
         super().__init__(bbox)
+
+        # Initialize features list
         self.features: list[np.ndarray] = []
         if feature is not None:
             self.features.append(feature)

trackers/core/deepsort/tracker.py

@@ -292,7 +292,8 @@ def _get_associated_indices(
         if combined_dist.size > 0:
             row_indices, col_indices = np.where(combined_dist < 1.0)
             sorted_pairs = sorted(
-                zip(row_indices, col_indices), key=lambda x: combined_dist[x[0], x[1]]
+                zip(map(int, row_indices), map(int, col_indices)),
+                key=lambda x: combined_dist[x[0], x[1]],
             )
 
             used_rows = set()
@@ -303,8 +304,10 @@ def _get_associated_indices(
                     used_cols.add(col)
                     matched_indices.append((row, col))
 
-            unmatched_trackers = unmatched_trackers - used_rows
-            unmatched_detections = unmatched_detections - used_cols
+            unmatched_trackers = unmatched_trackers - {int(row) for row in used_rows}
+            unmatched_detections = unmatched_detections - {
+                int(col) for col in used_cols
+            }
 
         return matched_indices, unmatched_trackers, unmatched_detections