提出可感知遮挡的跟踪框架，显著提升复杂场景下的多目标追踪稳定性。
Occlusion-Aware SORT: Observing Occlusion for Robust Multi-Object Tracking
- 通过遮挡感知模块分析物体遮挡状态，降低背景干扰。
- 在DanceTrack上实现63.1% HOTA、64.2% IDF1，优于基线模型。
- 无需训练即可接入其他跟踪器，适合实际部署场景使用。
多目标跟踪（MOT）需分析视频序列中物体轨迹并统计数量，但2D MOT易受部分遮挡引发的位置代价混淆影响。为此，本文提出新型无训练、即插即用的遮挡感知排序框架OA-SORT，包含遮挡感知模块（OAM）、遮挡感知偏移（OAO）和偏差感知动量（BAM）。其中，OAM利用高斯图（GM）抑制背景干扰，分析物体遮挡状态；OAO与BAM则基于该状态信息缓解代价混淆与估计不稳定问题。在DanceTrack、SportsMOT和MOT17数据集上的全面评估表明，遮挡处理对MOT至关重要。在DanceTrack测试集上，OA-SORT取得63.1%（HOTA）和64.2%（IDF1）的性能。此外，将该框架集成至四个额外跟踪器后，平均提升HOTA 2.08%、IDF1 3.05%，验证了其可复用性。
原文摘要 · Abstract (English)
Multi-object tracking (MOT) involves analyzing object trajectories and counting the number of objects in video sequences. However, 2D MOT faces challenges due to positional cost confusion arising from partial occlusion. To address this issue, we present the novel Occlusion-Aware SORT (OA-SORT) framework, a plug-and-play and training-free framework that includes the Occlusion-Aware Module (OAM), the Occlusion-Aware Offset (OAO), and the Bias-Aware Momentum (BAM). Specifically, OAM analyzes the occlusion status of objects, where a Gaussian Map (GM) is introduced to reduce background influence. In contrast, OAO and BAM leverage the OAM-described occlusion status to mitigate cost confusion and suppress estimation instability. Comprehensive evaluations on the DanceTrack, SportsMOT, and MOT17 datasets demonstrate the importance of occlusion handling in MOT. On the DanceTrack test set, OA-SORT achieves 63.1% and 64.2% in HOTA and IDF1, respectively. Furthermore, integrating the Occlusion-Aware framework into the four additional trackers improves HOTA and IDF1 by an average of 2.08% and 3.05%, demonstrating the reusability of the occlusion awareness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。