  Non-overlapping multi-camera visual object tracking typically consists of two
steps: single camera object tracking and inter-camera object tracking. Most of
tracking methods focus on single camera object tracking, which happens in the
same scene, while for real surveillance scenes, inter-camera object tracking is
needed and single camera tracking methods can not work effectively. In this
paper, we try to improve the overall multi-camera 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 single
camera object tracking.

Computer Vision and Pattern Recognition