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Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targetsacross multiple cameras, is a crucial technique for smart city applications. Inthis paper, we propose an effective and reliable MTMCT framework for vehicles,which consists of a traffic-aware single camera tracking (TSCT) algorithm, atrajectory-based camera link model (CLM) for vehicle re-identification (ReID),and a hierarchical clustering algorithm to obtain the cross camera vehicletrajectories. First, the TSCT, which jointly considers vehicle appearance,geometric features, and some common traffic scenarios, is proposed to track thevehicles in each camera separately. Second, the trajectory-based CLM is adoptedto facilitate the relationship between each pair of adjacently connectedcameras and add spatio-temporal constraints for the subsequent vehicle ReIDwith temporal attention. Third, the hierarchical clustering algorithm is usedto merge the vehicle trajectories among all the cameras to obtain the finalMTMCT results. Our proposed MTMCT is evaluated on the CityFlow dataset andachieves a new state-of-the-art performance with IDF1 of 74.93%.

Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model