Starwit q scaling

boxmot/configs/deepocsort.yaml

@@ -14,3 +14,5 @@ iou_thresh: 0.3
 max_age: 30
 min_hits: 1
 w_association_emb: 0.75
+Q_xy_scaling: 0.01
+Q_s_scaling: 0.0001

boxmot/configs/ocsort.yaml

@@ -9,3 +9,5 @@ iou_thresh: 0.3
 max_age: 30
 min_hits: 1
 use_byte: false
+Q_xy_scaling: 0.01
+Q_s_scaling: 0.0001

boxmot/tracker_zoo.py

@@ -50,6 +50,8 @@ def create_tracker(tracker_type, tracker_config, reid_weights, device, half, per
             asso_func=cfg.asso_func,
             inertia=cfg.inertia,
             use_byte=cfg.use_byte,
+            Q_xy_scaling=cfg.Q_xy_scaling,
+            Q_s_scaling=cfg.Q_s_scaling
         )
         return ocsort
 
@@ -97,6 +99,8 @@ def create_tracker(tracker_type, tracker_config, reid_weights, device, half, per
             delta_t=cfg.delta_t,
             asso_func=cfg.asso_func,
             inertia=cfg.inertia,
+            Q_xy_scaling=cfg.Q_xy_scaling,
+            Q_s_scaling=cfg.Q_s_scaling
         )
         return deepocsort
     elif tracker_type == 'hybridsort':

boxmot/trackers/deepocsort/deep_ocsort.py

@@ -53,7 +53,7 @@ class KalmanBoxTracker(object):
 
     count = 0
 
-    def __init__(self, det, delta_t=3, emb=None, alpha=0, max_obs=50):
+    def __init__(self, det, delta_t=3, emb=None, alpha=0, max_obs=50, Q_xy_scaling = 0.01, Q_s_scaling = 0.0001):
         """
         Initialises a tracker using initial bounding box.
 
@@ -65,6 +65,9 @@ def __init__(self, det, delta_t=3, emb=None, alpha=0, max_obs=50):
         self.cls = det[5]
         self.det_ind = det[6]
 
+        self.Q_xy_scaling = Q_xy_scaling
+        self.Q_s_scaling = Q_s_scaling
+
         self.kf = KalmanFilterXYSR(dim_x=7, dim_z=4)
         self.kf.F = np.array(
             [
@@ -89,8 +92,9 @@ def __init__(self, det, delta_t=3, emb=None, alpha=0, max_obs=50):
         self.kf.R[2:, 2:] *= 10.0
         self.kf.P[4:, 4:] *= 1000.0  # give high uncertainty to the unobservable initial velocities
         self.kf.P *= 10.0
-        self.kf.Q[-1, -1] *= 0.01
-        self.kf.Q[4:, 4:] *= 0.01
+        self.kf.Q[4:6, 4:6] *= self.Q_xy_scaling
+        self.kf.Q[-1, -1] *= self.Q_s_scaling
+
         self.bbox_to_z_func = xyxy2xysr
         self.x_to_bbox_func = convert_x_to_bbox
 
@@ -238,6 +242,8 @@ def __init__(
         embedding_off=False,
         cmc_off=False,
         aw_off=False,
+        Q_xy_scaling=0.01,
+        Q_s_scaling=0.0001,
         **kwargs
     ):
         super().__init__(max_age=max_age)
@@ -255,6 +261,8 @@ def __init__(
         self.alpha_fixed_emb = alpha_fixed_emb
         self.aw_param = aw_param
         self.per_class = per_class
+        self.Q_xy_scaling = Q_xy_scaling
+        self.Q_s_scaling = Q_s_scaling
         KalmanBoxTracker.count = 1
 
         rab = ReidAutoBackend(
@@ -411,6 +419,8 @@ def update(self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None) ->
                 delta_t=self.delta_t,
                 emb=dets_embs[i],
                 alpha=dets_alpha[i],
+                Q_xy_scaling=self.Q_xy_scaling, 
+                Q_s_scaling=self.Q_s_scaling,                
                 max_obs=self.max_obs
             )
             self.active_tracks.append(trk)

boxmot/trackers/ocsort/ocsort.py

@@ -59,13 +59,17 @@ class KalmanBoxTracker(object):
 
     count = 0
 
-    def __init__(self, bbox, cls, det_ind, delta_t=3, max_obs=50):
+    def __init__(self, bbox, cls, det_ind, delta_t=3, max_obs=50, Q_xy_scaling = 0.01, Q_s_scaling = 0.0001):
         """
         Initialises a tracker using initial bounding box.
 
         """
         # define constant velocity model
         self.det_ind = det_ind
+
+        self.Q_xy_scaling = Q_xy_scaling
+        self.Q_s_scaling = Q_s_scaling
+
         self.kf = KalmanFilterXYSR(dim_x=7, dim_z=4, max_obs=max_obs)
         self.kf.F = np.array(
             [
@@ -92,8 +96,9 @@ def __init__(self, bbox, cls, det_ind, delta_t=3, max_obs=50):
             4:, 4:
         ] *= 1000.0  # give high uncertainty to the unobservable initial velocities
         self.kf.P *= 10.0
-        self.kf.Q[-1, -1] *= 0.01
-        self.kf.Q[4:, 4:] *= 0.01
+
+        self.kf.Q[4:6, 4:6] *= self.Q_xy_scaling
+        self.kf.Q[-1, -1] *= self.Q_s_scaling
 
         self.kf.x[:4] = xyxy2xysr(bbox)
         self.time_since_update = 0
@@ -189,6 +194,8 @@ def __init__(
         asso_func="iou",
         inertia=0.2,
         use_byte=False,
+        Q_xy_scaling=0.01,
+        Q_s_scaling=0.0001
     ):
         super().__init__(max_age=max_age)
         """
@@ -204,6 +211,8 @@ def __init__(
         self.asso_func = get_asso_func(asso_func)
         self.inertia = inertia
         self.use_byte = use_byte
+        self.Q_xy_scaling = Q_xy_scaling
+        self.Q_s_scaling = Q_s_scaling
         KalmanBoxTracker.count = 0
 
     @PerClassDecorator
@@ -341,7 +350,7 @@ def update(self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None) ->
 
         # create and initialise new trackers for unmatched detections
         for i in unmatched_dets:
-            trk = KalmanBoxTracker(dets[i, :5], dets[i, 5], dets[i, 6], delta_t=self.delta_t, max_obs=self.max_obs)
+            trk = KalmanBoxTracker(dets[i, :5], dets[i, 5], dets[i, 6], delta_t=self.delta_t, Q_xy_scaling=self.Q_xy_scaling, Q_s_scaling=self.Q_s_scaling, max_obs=self.max_obs)
             self.active_tracks.append(trk)
         i = len(self.active_tracks)
         for trk in reversed(self.active_tracks):
@@ -368,4 +377,4 @@ def update(self, dets: np.ndarray, img: np.ndarray, embs: np.ndarray = None) ->
                 self.active_tracks.pop(i)
         if len(ret) > 0:
             return np.concatenate(ret)
-        return np.array([])
+        return np.array([])

tests/unit/test_trackers.py

@@ -9,6 +9,10 @@
     StrongSORT, BoTSORT, DeepOCSORT, OCSORT, BYTETracker, get_tracker_config, create_tracker,
 )
 
+from boxmot.trackers.ocsort.ocsort import KalmanBoxTracker as OCSortKalmanBoxTracker
+from boxmot.trackers.deepocsort.deep_ocsort import KalmanBoxTracker as DeepOCSortKalmanBoxTracker
+
+
 
 MOTION_ONLY_TRACKING_METHODS=[OCSORT, BYTETracker]
 MOTION_N_APPEARANCE_TRACKING_METHODS=[StrongSORT, BoTSORT, DeepOCSORT]
@@ -59,6 +63,61 @@ def test_dynamic_max_obs_based_on_max_age():
     assert ocsort.max_obs == (max_age + 5)
 
 
+def create_kalman_box_tracker_ocsort(bbox, cls, det_ind, tracker):
+    return OCSortKalmanBoxTracker(
+        bbox,
+        cls,
+        det_ind,
+        Q_xy_scaling=tracker.Q_xy_scaling,
+        Q_s_scaling=tracker.Q_s_scaling
+    )
+
+
+def create_kalman_box_tracker_deepocsort(bbox, cls, det_ind, tracker):
+    # DeepOCSort KalmanBoxTracker expects input in different format than OCSort
+    det = np.concatenate([bbox, [cls, det_ind]]) 
+    return DeepOCSortKalmanBoxTracker(
+        det,
+        Q_xy_scaling=tracker.Q_xy_scaling,
+        Q_s_scaling=tracker.Q_s_scaling
+    )
+
+
+TRACKER_CREATORS = {
+    OCSORT: create_kalman_box_tracker_ocsort,
+    DeepOCSORT: create_kalman_box_tracker_deepocsort,
+}
+
+
+@pytest.mark.parametrize("Tracker, init_args", [
+    (OCSORT, {}),
+    (DeepOCSORT, {
+        'model_weights': Path(WEIGHTS / 'osnet_x0_25_msmt17.pt'),
+        'device': 'cpu',
+        'fp16': True
+    }),
+])
+def test_Q_matrix_scaling(Tracker, init_args):
+    bbox = np.array([0, 0, 100, 100, 0.9])
+    cls = 1
+    det_ind = 0
+    Q_xy_scaling = 0.05
+    Q_s_scaling = 0.0005
+
+    tracker = Tracker(
+        Q_xy_scaling=Q_xy_scaling, 
+        Q_s_scaling=Q_s_scaling,
+        **init_args
+    )
+
+    create_kalman_box_tracker = TRACKER_CREATORS[Tracker]
+    kalman_box_tracker = create_kalman_box_tracker(bbox, cls, det_ind, tracker)
+
+    assert kalman_box_tracker.kf.Q[4, 4] == Q_xy_scaling, "Q_xy scaling incorrect for x' velocity"
+    assert kalman_box_tracker.kf.Q[5, 5] == Q_xy_scaling, "Q_xy scaling incorrect for y' velocity"
+    assert kalman_box_tracker.kf.Q[6, 6] == Q_s_scaling, "Q_s scaling incorrect for s' (scale) velocity"
+
+
 @pytest.mark.parametrize("tracker_type", PER_CLASS_TRACKERS)
 def test_per_class_tracker_output_size(tracker_type):
 

tracking/evolve.py

@@ -155,7 +155,8 @@ def get_new_config(self, trial):
             delta_t = trial.suggest_int("delta_t", 1, 5, step=1)
             asso_func = trial.suggest_categorical("asso_func", ['iou', 'giou', 'centroid'])
             inertia = trial.suggest_float("inertia", 0.1, 0.4)
-            use_byte = trial.suggest_categorical("use_byte", [True, False])
+            Q_xy_scaling = trial.suggest_float("Q_xy_scaling", 0.01, 1)
+            Q_s_scaling = trial.suggest_float("Q_s_scaling", 0.0001, 1)
 
             d = {
                 'det_thresh': det_thresh,
@@ -165,7 +166,9 @@ def get_new_config(self, trial):
                 'delta_t': delta_t,
                 'asso_func': asso_func,
                 'inertia': inertia,
-                'use_byte': use_byte
+                'use_byte': use_byte,
+                'Q_xy_scaling': Q_xy_scaling,
+                'Q_s_scaling': Q_s_scaling
             }
 
         elif self.opt.tracking_method == 'deepocsort':
@@ -183,6 +186,8 @@ def get_new_config(self, trial):
             embedding_off = trial.suggest_categorical("embedding_off", [True, False])
             cmc_off = trial.suggest_categorical("cmc_off", [True, False])
             aw_off = trial.suggest_categorical("aw_off", [True, False])
+            Q_xy_scaling = trial.suggest_float("Q_xy_scaling", 0.01, 1)
+            Q_s_scaling = trial.suggest_float("Q_s_scaling", 0.0001, 1)
 
             d = {
                 'det_thresh': det_thresh,
@@ -198,6 +203,8 @@ def get_new_config(self, trial):
                 'embedding_off': embedding_off,
                 'cmc_off': cmc_off,
                 'aw_off': aw_off,
+                'Q_xy_scaling': Q_xy_scaling,
+                'Q_s_scaling': Q_s_scaling
             }
 
         # overwrite existing config for tracker