A novel method for moving object detection in foggy day

Intelligent visual surveillance system can work normally under clear weather. But under bad weather, especially in foggy days, it can not detect moving objects accurately due to low scene visibility. Our research aims to resolve this problem. This paper presents a novel method for moving object detection in foggy days. Firstly, surveillance video under foggy weather is defogged, leveraging a physics-based image restoration approach. Secondly, we exploit a novel background maintenance algorithm based on the Unscented Kalman Filter(UKF) to subtract the background from the defogged video. Finally, moving objects are segmented by background differencing. Evaluations are performed to verb the effectiveness and practicality of this approach. Experimental results show that our method can be applied in real time surveillance systems.