Unsupervised Change Detection in Wide-Field Video Images Under Low Illumination

In low-illumination environments such as at night, due to factors such as the large monitoring field of an eagle eye, short sensor exposure time, and high-density random noise, the video images collected by image sensors generally have poor visual quality and low signal-to-noise ratio, which makes it difficult for surveillance systems to detect weak changes. To solve this problem, we propose a method for image change detection (CD) in surveillance video based on optimized k-medoids clustering and adaptive fusion of difference images (DIs). First, for the input multitemporal video surveillance images, two DIs are obtained by log-ratio and extremum pixel ratio operators. Then, the two DIs are adaptively fused by combining the local energy of DIs and the Laplacian pyramid. Simultaneously, the fused DI is compressed by the normalization function, and the final DI is obtained via the improved adaptive median filter. Finally, the changed image is obtained by using the optimized k-medoids clustering algorithm. The experimental results show that the proposed method can accurately and effectively detect weak changes in the eagle eye surveillance picture in a low-illumination environment. Compared with those of other methods, the accuracy and robustness of the proposed method are higher, and the running time of the algorithm is shorter. Moreover, it will not generate a false alarm due to the influence of noise in unchanged scenes.