Change detection in multitemporal monitoring images under low illumination

Video surveillance may involve the simultaneous monitoring of a large number of areas. Automatic change detection of a monitoring area (such as involving the movement of people or vehicle can reduce risks incurred in negligent manual observation. However, the low signal-to-noise ratio (SNR) dark environments can significantly corrupt camera images, making it difficult for machine learning surveillance systems to detect small changes in monitored images. In
addition, in the absence of changes between two multitemporal monitoring images, sensor noise can lead to false alarms. The objective of the paper is to reduce the effect of sensor noise on change detection of monitored images and the run time change detection algorithms. For these purposes, we proposed a novel multitemporal monitoring image change detection algorithm
based on morphological structure filtering and normalized fusion difference image. First, the random noise in two surveillance images was removed using a multidirectional weight multiscale series of a morphological filter. Next, two difference images were obtained by using compression log-ratio operator and the mean ratio operator, and a fusion difference image was obtained equal-weight fusion of the two difference images. Then, the sigmoid function was used to compress the difference map to obtain a normalized fusion difference image, and a median filter was used to obtain a final difference image. Finally, the k-means clustering algorithm was utilized to obtain the change detection result. The experimental results demonstrate that the proposed method can accurately detect changes in a night monitoring scene in real time. Subjective and objective evaluation of the experimental results demonstrated that the proposed method is superior to reference algorithms in terms of change detection accuracy, time, and robustness.