Moving target detection based on improved ghost suppression and adaptive visual background extraction

Abstract

Visual background extraction algorithm (ViBe) uses the first frame image to initialize the background model, which can easily introduce the “ghost”. Because ViBe uses the fixed segmentation threshold to achieve the foreground and background segmentation, the detection results in many false detections for the highly dynamic background. To solve these problems, an improved ghost suppression and adaptive Visual Background Extraction algorithm is proposed in this paper. Firstly, with the pixel’s temporal and spatial information, the historical pixels of a certain combination are used to initialize the background model in the odd frames of the video sequence. Secondly, the background sample set combined with the neighborhood pixels are used to determine a complex degree of the background, to acquire the adaptive segmentation threshold. Thirdly, the update rate is adjusted based on the complexity of the background. Finally, the detected result goes through a post-processing to achieve better detection results. The experimental results show that the improved algorithm will not only quickly suppress the “ghost”, but also have a better detection in a complex dynamic background.