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