Merge pull request #1854 from Fleyderer/master

Added min confidence threshold to existing trackers

boxmot/configs/bytetrack.yaml

@@ -1,6 +1,6 @@
-min_thresh:
+min_conf:
   type: uniform
-  default: 0.2  # from the default parameters
+  default: 0.1  # from the default parameters
   range: [0.1, 0.3]
 
 track_thresh:

boxmot/configs/ocsort.yaml

@@ -1,3 +1,8 @@
+min_conf:
+  type: uniform
+  default: 0.1  # from the default parameters
+  range: [0.1, 0.3]
+
 det_thresh:
   type: uniform
   default: 0.6  # from the default parameters

boxmot/configs/strongsort.yaml

@@ -1,3 +1,8 @@
+min_conf:
+  type: uniform
+  default: 0.6  # from the default parameters
+  range: [0.2, 0.8]
+
 ema_alpha:
   type: uniform
   default: 0.9  # from the default parameters

boxmot/trackers/bytetrack/bytetrack.py

@@ -120,7 +120,7 @@ class ByteTrack(BaseTracker):
     BYTETracker: A tracking algorithm based on ByteTrack, which utilizes motion-based tracking.
 
     Args:
-        min_thresh (float, optional): Threshold for detection confidence. Detections below this threshold are discarded.
+        min_conf (float, optional): Threshold for detection confidence. Detections below this threshold are discarded.
         track_thresh (float, optional): Threshold for detection confidence. Detections above this threshold are considered for tracking in the first association round.
         match_thresh (float, optional): Threshold for the matching step in data association. Controls the maximum distance allowed between tracklets and detections for a match.
         track_buffer (int, optional): Number of frames to keep a track alive after it was last detected. A longer buffer allows for more robust tracking but may increase identity switches.
@@ -129,7 +129,7 @@ class ByteTrack(BaseTracker):
     """
     def __init__(
         self,
-        min_thresh: float = 0.1,
+        min_conf: float = 0.1,
         track_thresh: float = 0.45,
         match_thresh: float = 0.8,
         track_buffer: int = 25,
@@ -145,7 +145,7 @@ def __init__(
         self.track_buffer = track_buffer
 
         self.per_class = per_class
-        self.min_thresh = min_thresh
+        self.min_conf = min_conf
         self.track_thresh = track_thresh
         self.match_thresh = match_thresh
         self.det_thresh = track_thresh
@@ -169,7 +169,7 @@ def update(self, dets: np.ndarray, img: np.ndarray = None, embs: np.ndarray = No
 
         remain_inds = confs > self.track_thresh
 
-        inds_low = confs > 0.1
+        inds_low = confs > self.min_conf
         inds_high = confs < self.track_thresh
         inds_second = np.logical_and(inds_low, inds_high)
 

boxmot/trackers/deepocsort/deepocsort.py

@@ -326,7 +326,7 @@ def update(self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None) ->
         if self.embedding_off or dets.shape[0] == 0:
             dets_embs = np.ones((dets.shape[0], 1))
         elif embs is not None:
-            dets_embs = embs
+            dets_embs = embs[remain_inds]
         else:
             # (Ndets x X) [512, 1024, 2048]
             dets_embs = self.model.get_features(dets[:, 0:4], img)

boxmot/trackers/ocsort/ocsort.py

@@ -203,6 +203,7 @@ class OcSort(BaseTracker):
     def __init__(
         self,
         per_class: bool = False,
+        min_conf: float = 0.1,
         det_thresh: float = 0.2,
         max_age: int = 30,
         min_hits: int = 3,
@@ -219,6 +220,7 @@ def __init__(
         Sets key parameters for SORT
         """
         self.per_class = per_class
+        self.min_conf = min_conf
         self.max_age = max_age
         self.min_hits = min_hits
         self.asso_threshold = asso_threshold
@@ -251,7 +253,7 @@ def update(self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None) ->
         dets = np.hstack([dets, np.arange(len(dets)).reshape(-1, 1)])
         confs = dets[:, 4+self.is_obb] 
 
-        inds_low = confs > 0.1
+        inds_low = confs > self.min_conf
         inds_high = confs < self.det_thresh
         inds_second = np.logical_and(
             inds_low, inds_high

boxmot/trackers/strongsort/strongsort.py

@@ -36,6 +36,7 @@ def __init__(
         device: device,
         half: bool,
         per_class: bool = False,
+        min_conf: float = 0.1,
         max_cos_dist=0.2,
         max_iou_dist=0.7,
         max_age=30,
@@ -46,6 +47,7 @@ def __init__(
     ):
 
         self.per_class = per_class
+        self.min_conf = min_conf
         self.model = ReidAutoBackend(
             weights=reid_weights, device=device, half=half
         ).model
@@ -76,6 +78,9 @@ def update(self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None) ->
         ), "Unsupported 'dets' 2nd dimension lenght, valid lenghts is 6"
 
         dets = np.hstack([dets, np.arange(len(dets)).reshape(-1, 1)])
+        remain_inds = dets[:, 4] >= self.min_conf
+        dets = dets[remain_inds]
+
         xyxy = dets[:, 0:4]
         confs = dets[:, 4]
         clss = dets[:, 5]
@@ -88,7 +93,7 @@ def update(self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None) ->
 
         # extract appearance information for each detection
         if embs is not None:
-            features = embs
+            features = embs[remain_inds]
         else:
             features = self.model.get_features(xyxy, img)