用高效信息传递提升Transformer追踪速度，实现实时多目标跟踪。
FastTrackTr:Towards Fast Multi-Object Tracking with Transformers
- 基于原始联合检测与追踪框架，改进帧间信息传递机制。
- 减少追踪所需查询数量，保持模型简洁且推理更快。
- 在多个数据集上实现实时追踪，精度优于或媲美现有方法。
基于Transformer的多目标追踪（MOT）方法近年来受到广泛关注，但其结构常导致推理速度慢。本文回溯经典联合检测与追踪（JDT）方法，结合先进理论，在DETR基础上构建一种高效的帧间信息传递机制，提出快速新型JDT型MOT框架FastTrackTr。该方法不仅显著减少追踪阶段的查询数量，还避免引入过多网络结构，保持模型轻量。实验表明，该方法具备实现实时追踪的潜力，并在多个数据集上达到具有竞争力的追踪精度。
原文摘要 · Abstract (English)
Transformer-based multi-object tracking (MOT) methods have captured the attention of many researchers in recent years. However, these models often suffer from slow inference speeds due to their structure or other issues. To address this problem, we revisited the Joint Detection and Tracking (JDT) method by looking back at past approaches. By integrating the original JDT approach with some advanced theories, this paper employs an efficient method of information transfer between frames on the DETR, constructing a fast and novel JDT-type MOT framework: FastTrackTr. Thanks to the superiority of this information transfer method, our approach not only reduces the number of queries required during tracking but also avoids the excessive introduction of network structures, ensuring model simplicity. Experimental results indicate that our method has the potential to achieve real-time tracking and exhibits competitive tracking accuracy across multiple datasets.
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