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.

翻译：GRAP-MOT是一种解决人员多目标跟踪问题的新方法，专门针对具有重叠多摄像头视角的封闭区域视频，其中人员遮挡频繁发生。我们新颖的图加权解决方案基于轨迹和人员特征在线更新其身份标签。为寻求最优解，我们深入研究了多目标跟踪流程的所有要素，包括特征提取、跟踪与社区搜索。此外，GRAP-MOT配备了人员位置估计模块，为多目标跟踪方法提供了额外的关键信息，确保其性能优于无位置数据的方法。我们在封闭区域模型采集的录像以及满足高度拥挤空间要求的公开真实数据集上测试了GRAP-MOT，结果证明了我们方案的优越性。最后，我们分析了用于比较多目标跟踪算法的现有评估指标，并得出结论：在此类比较中，IDF1比MOTA更为适用。我们已将代码及采集的数据集公开提供。