  Multi-object tracking (MOT) aims at estimating bounding boxes and identities
of objects in videos. Most methods can be roughly classified as
tracking-by-detection and joint-detection-association paradigms. Although the
latter has elicited more attention and demonstrates comparable performance
relative than the former, we claim that the tracking-by-detection paradigm is
still the optimal solution in terms of tracking accuracy,such as
ByteTrack,which achieves 80.3 MOTA, 77.3 IDF1 and 63.1 HOTA on the test set of
MOT17 with 30 FPS running speed on a single V100 GPU.However, under complex
perspectives such as vehicle and UAV acceleration, the performance of such a
tracker using uniform Kalman filter will be greatly affected, resulting in
tracking loss.In this paper, we propose a variable speed Kalman filter
algorithm based on environmental feedback and improve the matching process,
which can greatly improve the tracking effect in complex variable speed scenes
while maintaining high tracking accuracy in relatively static scenes.
Eventually, higher MOTA and IDF1 results can be achieved on MOT17 test set than
ByteTrack
