Background subtraction for night videos

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.

Automated summary

This paper proposes a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit to extract features of foreground objects and improves detection through a local pattern enhancement method. By designing features specific to evening light conditions, the method successfully addresses the issue of poor foreground object detection in night videos using existing background subtraction methods.