PuTR: A Pure Transformer for Decoupled and Online Multi-Object Tracking

• URL: http://arxiv.org/abs/2405.14119v1
• Date: Thu, 23 May 2024 02:44:46 GMT
• Title: PuTR: A Pure Transformer for Decoupled and Online Multi-Object Tracking
• Authors: Chongwei Liu, Haojie Li, Zhihui Wang, Rui Xu,
• Abstract summary: We show that a pure Transformer can unify short- and long-term associations in a decoupled and online manner. Experiments show that a classic Transformer architecture naturally suits the association problem and achieves a strong baseline. This work pioneers a promising Transformer-based approach for the MOT task, and provides code to facilitate further research.
• Score: 36.5272157173876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in Multi-Object Tracking (MOT) have achieved remarkable success in short-term association within the decoupled tracking-by-detection online paradigm. However, long-term tracking still remains a challenging task. Although graph-based approaches can address this issue by modeling trajectories as a graph in the decoupled manner, their non-online nature poses obstacles for real-time applications. In this paper, we demonstrate that the trajectory graph is a directed acyclic graph, which can be represented by an object sequence arranged by frame and a binary adjacency matrix. It is a coincidence that the binary matrix matches the attention mask in the Transformer, and the object sequence serves exactly as a natural input sequence. Intuitively, we propose that a pure Transformer can naturally unify short- and long-term associations in a decoupled and online manner. Our experiments show that a classic Transformer architecture naturally suits the association problem and achieves a strong baseline compared to existing foundational methods across four datasets: DanceTrack, SportsMOT, MOT17, and MOT20, as well as superior generalizability in domain shift. Moreover, the decoupled property also enables efficient training and inference. This work pioneers a promising Transformer-based approach for the MOT task, and provides code to facilitate further research. https://github.com/chongweiliu/PuTR

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