论文标题
通过邻居图来增强多对象跟踪中的关联
Enhancing the Association in Multi-Object Tracking via Neighbor Graph
论文作者
论文摘要
大多数现代的多对象跟踪（MOT）系统遵循逐个检测范式的跟踪。它首先定位感兴趣的对象，然后提取其各个外观特征以建立数据关联。但是，个体特征易受遮挡，照明变化和不准确检测的负面影响，从而导致关联推断的不匹配。在这项工作中，我们建议通过充分利用相邻信息来解决此问题。我们的动机来自人们倾向于在一个小组中移动的观察结果。因此，当一个单个目标的外观严重改变时，我们仍然可以在其邻居的帮助下识别它。为此，我们首先利用跟踪自我产生的时空关系有效地为目标选择合适的邻居。随后，我们构建了目标的邻居图，然后邻居使用图形卷积网络（GCN）学习图形特征。据我们所知，这是第一次通过MOT中的GCN利用邻居提示。最后，我们测试了MOT基准测试的方法，并在在线跟踪中实现最先进的性能。
Most modern multi-object tracking (MOT) systems follow the tracking-by-detection paradigm. It first localizes the objects of interest, then extracting their individual appearance features to make data association. The individual features, however, are susceptible to the negative effects as occlusions, illumination variations and inaccurate detections, thus resulting in the mismatch in the association inference. In this work, we propose to handle this problem via making full use of the neighboring information. Our motivations derive from the observations that people tend to move in a group. As such, when an individual target's appearance is seriously changed, we can still identify it with the help of its neighbors. To this end, we first utilize the spatio-temporal relations produced by the tracking self to efficiently select suitable neighbors for the targets. Subsequently, we construct neighbor graph of the target and neighbors then employ the graph convolution networks (GCN) to learn the graph features. To the best of our knowledge, it is the first time to exploit neighbor cues via GCN in MOT. Finally, we test our approach on the MOT benchmarks and achieve state-of-the-art performance in online tracking.
