An Equalized Global Graph Model-Based Approach for Multicamera Object Tracking

Nonoverlapping multicamera visual object tracking typically consists of two steps: single-camera object tracking (SCT) and inter-camera object tracking (ICT). Most of tracking methods focus on SCT, which happens in the same scene, while for real surveillance scenes, ICT is needed and single-camera tracking methods cannot work effectively. In this paper, we try to improve the overall multicamera object tracking performance by a global graph model with an improved similarity metric. Our method treats the similarities of single-camera tracking and inter-camera tracking differently and obtains the optimization in a global graph model. The results show that our method can work better even in the condition of poor SCT.