[AAAI 2024] UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation. UCMCTrack achieves SOTA on MOT17 using estimated camera parameters.
Thank you for your excellent paper, and this open source project. I really want to try it on my YOLOv8-OBB. However, with built-in tracker of the YOLO model, there are only two choice BOTSORT and BYTETRACK. I cannot find any instruction to integrate a custom tracker into YOLOv8. May you kindly provide some steps of using UCMCTrack+ in YOLOv8-OBB? Thank you! 中文的指引也可以,谢谢
currently I am using botsort to track:
model = YOLO("best.pt").to('cuda:0')
results = model.track(frame, persist=True, tracker="botsort.yaml", conf=0.691, iou=0.25)
if results[0].obb is not None:
boxes = results[0].obb.xyxyxyxy.cpu().numpy()
clss = results[0].obb.cls.cpu().tolist()
track_ids = results[0].obb.id.int().cpu().tolist() if results[0].obb.id is not None else [None] * len(clss)
confs = results[0].obb.conf.cpu().tolist()
detections = []
for box, cls, track_id, conf in zip(boxes, clss, track_ids, confs):
# Process box data
box = np.array(box).reshape(-1, 2)
center_x = box[:, 0].mean()
center_y = box[:, 1].mean()
width = box[:, 0].max() - box[:, 0].min()
height = box[:, 1].max() - box[:, 1].min()
detections.append((int(center_x), int(center_y), int(width), int(height), int(cls), track_id, conf, box))
frame = process_frame(frame, detections, track_ids)
cv2.imshow("YOLOv10 OBB Detection", frame)
Hello Kefu,
Thank you for your excellent paper, and this open source project. I really want to try it on my YOLOv8-OBB. However, with built-in tracker of the YOLO model, there are only two choice BOTSORT and BYTETRACK. I cannot find any instruction to integrate a custom tracker into YOLOv8. May you kindly provide some steps of using UCMCTrack+ in YOLOv8-OBB? Thank you! 中文的指引也可以,谢谢
currently I am using botsort to track: