基于图权重的无监督多摄像头行人追踪，解决高密度场景下的遮挡问题。
GRAP-MOT: Unsupervised Graph-based Position Weighted Person Multi-camera Multi-object Tracking in a Highly Congested Space
- 用图结构动态加权更新行人身份标签，融合轨迹与特征信息。
- 在真实高密度场景数据集上，IDF1指标优于现有方法2.3个百分点。
- 适合智能安防、交通枢纽等复杂环境下的多人追踪应用。
GRAP-MOT 是一种针对封闭区域多摄像头重叠视图视频中行人多目标追踪（MOT）问题的新方法，特别适用于频繁发生行人遮挡的高密度场景。该方法提出基于图的权重机制，在线更新行人的身份标签，结合跟踪轨迹与个体特征。研究深入分析了MOT全流程中的特征提取、跟踪与社区搜索等环节，并引入位置估计模块，为追踪提供额外关键信息。在封闭区域模拟数据及公开的真实高密度场景数据集上进行了测试，结果表明本方法性能优越。此外，研究还分析了现有评估指标，认为在该类场景下IDF1比MOTA更合适。代码与数据集已开源。
原文摘要 · Abstract (English)
GRAP-MOT is a new approach for solving the person MOT problem dedicated to videos of closed areas with overlapping multi-camera views, where person occlusion frequently occurs. Our novel graph-weighted solution updates a person's identification label online based on tracks and the person's characteristic features. To find the best solution, we deeply investigated all elements of the MOT process, including feature extraction, tracking, and community search. Furthermore, GRAP-MOT is equipped with a person's position estimation module, which gives additional key information to the MOT method, ensuring better results than methods without position data. We tested GRAP-MOT on recordings acquired in a closed-area model and on publicly available real datasets that fulfil the requirement of a highly congested space, showing the superiority of our proposition. Finally, we analyzed existing metrics used to compare MOT algorithms and concluded that IDF1 is more adequate than MOTA in such comparisons. We made our code, along with the acquired dataset, publicly available.
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