Comments (2)
Thank you for showing interest in our work. Your understanding is essentially correct.
- We take into account multiple temporal intervals for the velocity direction for tracklet-to-tracklet. However, for tracklet-to-detection associations, we employ a fixed interval as OC-SORT.
- Regarding the predicted scores of trajectories, we utilize either the Kalman Filter or Linear Prediction in the first or second association stage. We have conducted further ablation studies, and you can find the results in Table 5. We believe that the crucial factor in making this choice is the precision of the estimation.
Once again, we appreciate your interest and hope this explanation provides the clarity you were seeking. Please let us know if you have any more questions or require further information.
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My problems have been solved. Thank you for your reply!
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Related Issues (20)
- What is the running speed of the method described in this article? Can it be deployed on Jetson NX hardware for real-time tracking? What might be the final FPS (Frames Per Second)?thanks HOT 1
- The parameters are not used? HOT 2
- About the use of detectors HOT 4
- Retrieving lost tracklet information HOT 2
- How can I use it on Android? HOT 4
- What does train/val/test_segmap mean? HOT 1
- Adjusting max_age parameter HOT 2
- Question about the publication of the paper HOT 3
- demo_track HOT 1
- 关于用自己的数据集训练detector的问题 HOT 2
- Weighted HMIoU HOT 1
- segmentation fault (core dumped) HOT 2
- MOT20-test HOT 8
- Skip loading parameter 'heads.veight' to the model HOT 1
- How can we use official YOLOX as the detector? HOT 1
- self-trained detector HOT 1
- 为什么ReID的提升效果这么好? HOT 2
- hybrid_sort.py HOT 2
- Unable to reproduce the dancetrack effect in the paper HOT 5
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