Rethinking Joint Detection and Embedding for Multiobject Tracking in Multiscenario
Libin Xu, Yingping Huang
Abstract
The joint detection and embedding (JDE) paradigm has attracted considerable attention in multiobject tracking due to its effectiveness and efficiency. However, there are some issues that hinder further improvement in the performance of JDE-based methods. These issues include feature competition between detection and reidentification tasks, as well as the underutilization of detections in data association. Furthermore, the applicability of the JDE-based methods in multiscenario is limited because they focus on optimization in a single scenario. To tackle these issues, we rethink the JDE paradigm from two critical aspects, i.e., feature representation and data association. Based on this, a powerful JDE-based tracking method is proposed, named MMTrack. Specifically, a feature decoupling network is proposed to mitigate competition between detection and reidentification tasks. In addition, a hierarchical association strategy is designed to maximize object matching by considering nearly all detections. Finally, comprehensive experiments are conducted on public datasets covering multiscenario, which demonstrate the superiority of MMTrack.