KEYWORDS: Image segmentation, RGB color model, Video surveillance, Video, Image processing, Data modeling, Video processing, Machine vision, Computer vision technology, Visual process modeling
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.
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