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. The image foreground object detection must face the problems of moving background, illumination changes, chaotic, etc. in real word applications. In our approach, the 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 the homogeneous background subtraction that can detect the foreground object with less record memory. 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 then updated quickly to overcome the background variation. The proposed approach can improve the averages Error Rate of foreground object detection up to 86% when comparing with the latest works.
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