Effective Gaussian mixture learning and shadow suppression for video foreground segmentation
Yong Wang, Jinwen Tian, Yihua Tan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Robust and efficient foreground segmentation is a crucial topic in many computer vision applications. In this paper, we propose an improved method of foreground segmentation with the Gaussian mixture model (GMM) for video surveillance. The number of mixture components of GMM is estimated according to the frequency of pixel value changes, the performance of GMM can be effectively enhanced with the modified background learning and update, new Gaussian distribution generation rule and shadow detection. In order to improve the efficiency, illumination assessment is used to decide whether there are shadows in the given image. Shadow suppression will be adopted based on morphological reconstruction. Besides, the detection of sudden illumination change and background updating are also presented. Results obtained with different real-world scenarios show the robustness and efficiency of the approach.