Distributed multi-camera multi-target association for real-time tracking
Senquan Yang, Fan Ding, Pu Li, Songxi Hu
Abstract
Tracking and associating different views of the same target across moving cameras is challenging as its appearance, pose and scale may vary greatly. Moreover, with multiple targets a management module is needed for new targets entering and old targets exiting the field of view of each camera. To address these challenges, we propose DMMA, a Distributed Multi-camera Multi-target Association for real-time tracking that employs a target management module coupled with a local data-structure containing the information on the targets. The target management module shares appearance and label information for each known target for inter-camera association. DMMA is designed as a distributed target association that allows a camera to join at any time, does not require cross-camera calibration, and can deal with target appearance and disappearance. The various parts of DMMA are validated using benchmark datasets and evaluation criteria. Moreover, we introduce a new mobile-camera dataset comprising six different scenes with moving cameras and objects, where DMMA achieves 92% MCTA on average. Experimental results show that the proposed tracker achieves a good association accuracy and speed trade-off by working at 32 frames per second (fps) with high definition (HD) videos.