Effective Gaussian mixture learning and shadow suppression for video foreground segmentation

Yong Wang; Jinwen Tian; Yihua Tan

doi:10.1117/12.748233

15 November 2007 Effective Gaussian mixture learning and shadow suppression for video foreground segmentation

Yong Wang, Jinwen Tian, Yihua Tan

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67861D (2007) https://doi.org/10.1117/12.748233
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

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.

Citation Download Citation

Yong Wang, Jinwen Tian, and Yihua Tan "Effective Gaussian mixture learning and shadow suppression for video foreground segmentation", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67861D (15 November 2007); https://doi.org/10.1117/12.748233

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