This is our 2nd-place solution for the ECCV 2022 Multiple People Tracking in Group Dance Challenge. Our method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering. Our C-BIoU tracker adds buffers to expand the matching space of detections and tracks, which mitigates the effect of irregular motions in two aspects: one is to directly match identical but non-overlapping detections and tracks in adjacent frames, and the other is to compensate for the motion estimation bias in the matching space. In addition, to reduce the risk of overexpansion of the matching space, cascaded matching is employed: first matching alive tracks and detections with a small buffer, and then matching unmatched tracks and detections with a large buffer. After using our C-BIoU for online tracking, we applied the offline refinement introduced by ReMOTS.
翻译:这是我们的ECCV 2022多人在团体舞蹈挑战中追踪2022年多人的第二位解决方案。我们的方法主要包括两个步骤:使用我们的Cassaded buffer-IoU(C-BIOU)跟踪器进行在线短期跟踪,以及使用外观特征和等级组合进行离线长期跟踪。我们的C-BIOU跟踪器添加了缓冲,以扩大探测和轨道的匹配空间,这在两个方面减轻了不规则动议的影响:一是直接匹配相近但非重叠的探测和相邻框轨迹,另一是补偿匹配空间的运动估计偏差。此外,为了减少匹配空间过度扩展的风险,还采用了连锁匹配匹配:首先将活的跟踪和探测与小型缓冲匹配,然后将不匹配的跟踪和探测与大型缓冲匹配。在使用我们的C-BIOU进行在线跟踪后,我们采用了雷莫托斯的离线改进方法。