Multi-Camera Multi-Object Tracking (MC-MOT) utilizes information from multiple views to better handle problems with occlusion and crowded scenes. Recently, the use of graph-based approaches to solve tracking problems has become very popular. However, many current graph-based methods do not effectively utilize information regarding spatial and temporal consistency. Instead, they rely on single-camera trackers as input, which are prone to fragmentation and ID switch errors. In this paper, we propose a novel reconfigurable graph model that first associates all detected objects across cameras spatially before reconfiguring it into a temporal graph for Temporal Association. This two-stage association approach enables us to extract robust spatial and temporal-aware features and address the problem with fragmented tracklets. Furthermore, our model is designed for online tracking, making it suitable for real-world applications. Experimental results show that the proposed graph model is able to extract more discriminating features for object tracking, and our model achieves state-of-the-art performance on several public datasets.

提出了一种新颖的可重配置图模型，通过首先在空间上关联所有检测到的对象，然后将其重新配置为时间图来进行时间关联，从而能够提取出稳健的空间和时间感知特征，并解决跟踪过程中出现的分段轨迹问题。该模型适用于在线跟踪，并在多个公共数据集上取得了最先进的性能。

ReST: 可重构的时空图模型用于多摄像头多目标跟踪