Multi-object Tracking by Detection and Query: an efficient end-to-end manner

• URL: http://arxiv.org/abs/2411.06197v1
• Date: Sat, 09 Nov 2024 14:38:08 GMT
• Title: Multi-object Tracking by Detection and Query: an efficient end-to-end manner
• Authors: Shukun Jia, Yichao Cao, Feng Yang, Xin Lu, Xiaobo Lu,
• Abstract summary: Multi-object tracking is advancing through two dominant paradigms: traditional tracking by detection and newly emerging tracking by query. We propose the tracking-by-detection-and-query paradigm, which is achieved by a Learnable Associator. Compared to tracking-by-query models, LAID achieves competitive tracking accuracy with notably higher training efficiency.
• Score: 23.926668750263488
• License:
• Abstract: Multi-object tracking is advancing through two dominant paradigms: traditional tracking by detection and newly emerging tracking by query. In this work, we fuse them together and propose the tracking-by-detection-and-query paradigm, which is achieved by a Learnable Associator. Specifically, the basic information interaction module and the content-position alignment module are proposed for thorough information Interaction among object queries. Tracking results are directly Decoded from these queries. Hence, we name the method as LAID. Compared to tracking-by-query models, LAID achieves competitive tracking accuracy with notably higher training efficiency. With regard to tracking-by-detection methods, experimental results on DanceTrack show that LAID significantly surpasses the state-of-the-art heuristic method by 3.9% on HOTA metric and 6.1% on IDF1 metric. On SportsMOT, LAID also achieves the best score on HOTA metric. By holding low training cost, strong tracking capabilities, and an elegant end-to-end approach all at once, LAID presents a forward-looking direction for the field.

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