ValueError: shapes not aligned (when running deep sort on yolov8)

Question

I want to estimate the speed of a vehicle for that I am tracking the vehicle in a video. So I used a variant of deep sort to track and yolov8 for detections.

I tried yolov8 object detection, and deep sort object tracking to track vehicles, using the "Nicolai Nielsen" tutorials.

This is the traceback of my error.

ValueError                                Traceback (most recent call last)
<ipython-input-9-40ffc238945a> in <cell line: 26>()
     69     # Update tracker with bounding boxes
     70     print(list1)
---> 71     tracks = object_tracker.update_tracks(list1,frame)
     72 
     73     # Process each tracked object

8 frames
/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deepsort_tracker.py in update_tracks(self, raw_detections, embeds, frame, today, others, instance_masks)
    226         # Update tracker.
    227         self.tracker.predict()
--> 228         self.tracker.update(detections, today=today)
    229 
    230         return self.tracker.tracks

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/tracker.py in update(self, detections, today)
     95 
     96         # Run matching cascade.
---> 97         matches, unmatched_tracks, unmatched_detections = self._match(detections)
     98 
     99         # Update track set.

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/tracker.py in _match(self, detections)
    149             unmatched_tracks_a,
    150             unmatched_detections,
--> 151         ) = linear_assignment.matching_cascade(
    152             gated_metric,
    153             self.metric.matching_threshold,

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/linear_assignment.py in matching_cascade(distance_metric, max_distance, cascade_depth, tracks, detections, track_indices, detection_indices)
    145             continue
    146 
--> 147         matches_l, _, unmatched_detections = min_cost_matching(
    148             distance_metric,
    149             max_distance,

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/linear_assignment.py in min_cost_matching(distance_metric, max_distance, tracks, detections, track_indices, detection_indices)
     60         return [], track_indices, detection_indices  # Nothing to match.
     61 
---> 62     cost_matrix = distance_metric(tracks, detections, track_indices, detection_indices)
     63     cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
     64     # indices = linear_assignment(cost_matrix)

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/tracker.py in gated_metric(tracks, dets, track_indices, detection_indices)
    131             features = np.array([dets[i].feature for i in detection_indices])
    132             targets = np.array([tracks[i].track_id for i in track_indices])
--> 133             cost_matrix = self.metric.distance(features, targets)
    134             cost_matrix = linear_assignment.gate_cost_matrix(
    135                 self.kf, cost_matrix, tracks, dets, track_indices, detection_indices, only_position=self.gating_only_position

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/nn_matching.py in distance(self, features, targets)
    172         cost_matrix = np.zeros((len(targets), len(features)))
    173         for i, target in enumerate(targets):
--> 174             cost_matrix[i, :] = self._metric(self.samples[target], features)
    175         return cost_matrix

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/nn_matching.py in _nn_cosine_distance(x, y)
     93 
     94     """
---> 95     distances = _cosine_distance(x, y)
     96     return distances.min(axis=0)
     97 

/usr/local/lib/python3.10/dist-packages/deep_sort_realtime/deep_sort/nn_matching.py in _cosine_distance(a, b, data_is_normalized)
     52         a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
     53         b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
---> 54     return 1.0 - np.dot(a, b.T)
     55 
     56 

ValueError: shapes (2,1280,3) and (3,1280,2) not aligned: 3 (dim 2) != 1280 (dim 1)

code snippet:

while True:
    ret, frame = cap.read()
    print("frame:", frame.shape)
    if not ret:
        break
    count += 1
    # Perform object detection on the frame (assuming you have the model defined somewhere)
    results = model.predict(frame, save=True, conf=0.25)
    annotated=results[0].plot()
    cv2_imshow(annotated)
    z=results[0].boxes
    list1 = []

    cords=z.xywhn.cpu().numpy()
    confs=list(z.conf)
    cls_ids=(z.cls)
    for i in range(len(cords)):
        x1=cords[i][0]
        y1=cords[i][1]
        w=cords[i][2]
        h=cords[i][3]
        c=(confs[i].cpu()).item()
        d=str(cls_ids[i].item())
        list1.append(([x1,y1,w,h],c,d))             #([left,top,w,h],confidence,class_name)
    # Update tracker with bounding boxes
    print(list1)
    #here I am getting the error
    tracks = object_tracker.update_tracks(list1,frame)  
    # Process each tracked object
    print(tracks)
    for track in tracks:
  
        if not track.is_confirmed():
            continue
        tk_id = int(track.track_id)
        ltrb = track.to_ltrb()
        print(ltrb)
        x3,y3,x4,y4=ltrb
        cx=int(x3+x4)//2
        cy=int(y3+y4)//2

list1 formats: list1: outputs [([0.54669255, 0.41841677, 0.02166195, 0.050066292], 0.7329245805740356, '0.0'), ([0.45572662, 0.4183714, 0.019963264, 0.049137793], 0.6612600088119507, '0.0')]

Deep sort Initilaisation:

from deep_sort_realtime.deepsort_tracker import DeepSort

object_tracker = DeepSort(max_age=5,
                n_init=2,
                nms_max_overlap=0.9,
                max_cosine_distance=0.3,
                nn_budget=None,
                override_track_class = None,
                embedder="mobilenet",
                half=True,
                bgr=True,
                embedder_gpu=True,
                embedder_model_name=None,
                embedder_wts=None,
                polygon=False,
                today=None)

I am new to the object tracking, Someone provide me with the solution or any article related to this error. Input image size is (720, 1280, 3) Thanks in advance.