Robust background subtraction in HSV color space

In the new MPEG-4 video coding standard, automatic video object segmentation plays a key role in supporting object-oriented coding and enabling content-based functionalities. Background subtraction is one of the basic automatic video object segmentation methods. But various environmental illumination conditions often make it hard to work. A robust background subtraction method is presented in this paper. A statistical background model is first setup in this algorithm. Then the hypothesis testing is applied to the following frames to segment the video objects. The HSV color model is used and its color components are efficiently analyzed and treated separately so that the proposed algorithm can adapt to different environmental illumination conditions. Shadows are detected and a new background update algorithm is also presented based on the observation that the illumination changes are temporal and will not influence all the following frames. All of them contribute to the robustness of the method. The experimental results show that the proposed background subtraction method can automatically segment video objects robustly and accurately in various illuminating environments.