An improved ViBe-based approach for moving object detection

Moving object detection is a challenging task in the automatic monitoring field, which plays a crucial role in most video-based applications. The visual background extractor (ViBe) algorithm has been widely used to deal with this problem due to its high detection rate and low computational complexity. However, there are some shortcomings in the general ViBe algorithm, such as the ghost area problem and the dynamic background problem. To deal with these problems, an improved ViBe approach is presented in this paper. In the proposed approach, a mode background modeling method is used to accelerate the process of the ghost elimination. For the detection of moving object in dynamic background, a local adaptive threshold and update rate is proposed for the ViBe approach to detect foreground and update background. Furthermore, an improved shadow removal method is presented, which is based on the HSV color space combined with the edge detection method. Finally, some experiments were conducted, and the results show the efficiency and effectiveness of the proposed approach.