Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking
This work addresses a specific bottleneck in multi-camera tracking for surveillance applications, offering an incremental but effective enhancement to data association.
The paper tackles the misfit between global re-identification features and local matching in multi-target multi-camera tracking by proposing an adaptive affinity module that tailors affinity metrics to local scopes, resulting in significant improvements on CityFlow and DukeMTMC datasets.
Data associations in multi-target multi-camera tracking (MTMCT) usually estimate affinity directly from re-identification (re-ID) feature distances. However, we argue that it might not be the best choice given the difference in matching scopes between re-ID and MTMCT problems. Re-ID systems focus on global matching, which retrieves targets from all cameras and all times. In contrast, data association in tracking is a local matching problem, since its candidates only come from neighboring locations and time frames. In this paper, we design experiments to verify such misfit between global re-ID feature distances and local matching in tracking, and propose a simple yet effective approach to adapt affinity estimations to corresponding matching scopes in MTMCT. Instead of trying to deal with all appearance changes, we tailor the affinity metric to specialize in ones that might emerge during data associations. To this end, we introduce a new data sampling scheme with temporal windows originally used for data associations in tracking. Minimizing the mismatch, the adaptive affinity module brings significant improvements over global re-ID distance, and produces competitive performance on CityFlow and DukeMTMC datasets.