论文标题
ECCV 2022的第二名解决方案在集体舞蹈挑战赛中跟踪
The Second-place Solution for ECCV 2022 Multiple People Tracking in Group Dance Challenge
论文作者
论文摘要
这是我们针对ECCV 2022多人追踪团体舞蹈挑战赛的第二个解决方案。我们的方法主要包括两个步骤：使用级联的缓冲区（C-BIOU）跟踪器在线短期跟踪，以及使用外观功能和分层聚类的离线长期跟踪。我们的C-BIOU跟踪器添加了缓冲区以扩大探测和轨道的匹配空间，从而减轻了两个方面的不规则运动的影响：一个是直接匹配相同但不重叠的检测和相邻帧中的轨道，而另一个是为了补偿匹配空间中的运动估计偏见。此外，为了降低匹配空间过度膨胀的风险，采用了级联的匹配：首先使用较小的缓冲区进行匹配的活着轨道和检测，然后用大型缓冲区匹配无与伦比的轨道和检测。在使用我们的C-Biou进行在线跟踪之后，我们应用了Enlots引入的离线细化。
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.
