A Robust People Tracking Method in Multiple Cameras
Satoshi Yoshida, Jianquan Liu, Shoji Nishimura · 2019
This paper proposes a robust people tracking method on videos obtained from multiple cameras. Yoshida et al. proposed a method that combines re-identification and video tracking technologies to track people in multiple cameras. However, this method is not robust enough because it uses only one type of features. The method fails to track people when some type of features is not available or when we use features of which characteristics can be easily changed with time or place. We propose a method to enhance the robustness by utilizing multiple types of features in this paper. We can track people even if some type of features are not available. Experimental results showed that 76% of images obtained from the same person were identified in average. In this demo, we show the results of the proposed method.