High-accuracy background model for real-time video foreground object detection

Video foreground object detection faces the problems of moving backgrounds, illumination changes, chaotic motion in real word applications. This paper presents a hybrid pixel-based background (HPB) model, which is constructed by single stable record and multi-layer astable records after initial learning. This HPB model can be used for background subtraction to extract objects precisely in various complex scenes. Using the multi-layer astable records, we also propose a homogeneous background subtraction that can detect the foreground object with less memory load. Based on the benchmark videos, the experimental results show that single stable and 3-layer multi-layer astable records can be enough for background model construction and are updated quickly to overcome the background variation. The proposed approach can improve the average error rates of foreground object detection up to 86% when comparing with the latest works. Furthermore, our method can achieve real-time analysis for complex scenes on personal computers and embedded platforms.