Grunnleggende konsepter
End-to-end ID prediction in multiple object tracking streamlines the process and improves performance.
Sammendrag
The content discusses a new approach called MOTIP that treats object association as an end-to-end ID prediction problem. It eliminates the need for heuristic algorithms and achieves impressive state-of-the-art performance in various scenarios like DanceTrack, SportsMOT, and MOT17. The method involves using DETR for detection, a learnable ID dictionary for identities, and an ID Decoder for predicting IDs based on historical trajectories.
Directory:
- Abstract
- MOT challenges with heuristic methods.
- Introduction of MOTIP as an end-to-end solution.
- Object Tracking Paradigms
- Tracking-by-detection vs. tracking-by-query methods.
- Methodology
- Formulating MOT as an ID prediction problem.
- Architecture of MOTIP: DETR detector, learnable ID dictionary, and ID Decoder.
- Experiments & Results
- Performance comparison with state-of-the-art methods on DanceTrack, SportsMOT, and MOT17.
- Ablation Experiments
- Impact of training strategies, trajectory augmentation, self-attention in the ID Decoder, one-hot vs. learnable ID embedding.
- Conclusion
Statistikk
In this paper, we regard this object association task as an End-to-End in-context ID prediction problem and propose a streamlined baseline called MOTIP.
Without bells and whistles, our method achieves impressive state-of-the-art performance in complex scenarios like DanceTrack and SportsMOT.
Sitater
"Our method achieves impressive state-of-the-art performance in complex scenarios like DanceTrack and SportsMOT."
"We believe that MOTIP demonstrates remarkable potential and can serve as a starting point for future research."