  Multi-target multi-camera tracking (MTMCT) of vehicles, i.e. tracking
vehicles across multiple cameras, is a crucial application for the development
of smart city and intelligent traffic system. The main challenges of MTMCT of
vehicles include the intra-class variability of the same vehicle and
inter-class similarity between different vehicles and how to associate the same
vehicle accurately across different cameras under large search space. Previous
methods for MTMCT usually use hierarchical clustering of trajectories to
conduct cross camera association. However, the search space can be large and
does not take spatial and temporal information into consideration. In this
paper, we proposed a transformer-based camera link model with spatial and
temporal filtering to conduct cross camera tracking. Achieving 73.68% IDF1 on
the Nvidia Cityflow V2 dataset test set, showing the effectiveness of our
camera link model on multi-target multi-camera tracking.
