Video image change detection under a wide field of view in foggy weather

Addressing the problem of poor visual effect and noise interference in foggy video images collected by hawk-eye image sensors, we suggest an approach which utilizes difference images (DIs) fusion and two-stage clustering for detecting changes in monitoring video pictures. First, we utilize a dehazing algorithm based on the color attenuation prior theory to effectively eliminate the haze interference. Then, two DIs are generated using the compressed logarithmic ratio and maximum mean pixel ratio operators. Fusion of two DIs using multi-scale Laplace pyramid and regional energy algorithms, and filtered using the adaptive median filtering algorithm to obtain the final DIs. Finally, the soft threshold function and the FCM clustering algorithm are used to perform two-stage clustering on the DI to obtain the change detection result. Experimental results demonstrate that this approach can effectively and accurately detect subtle changes in hawk-eye surveillance video images under foggy conditions.