Multi-target multi-camera vehicle tracking using transformer-based camera link model and spatial-temporal information
This work addresses vehicle tracking across cameras for smart city applications, representing an incremental improvement over previous methods.
The paper tackled the problem of multi-target multi-camera vehicle tracking by proposing a transformer-based camera link model with spatial-temporal filtering to improve cross-camera association, achieving 73.68% IDF1 on the Nvidia Cityflow V2 dataset.
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.