  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.
