Foreground segmentation is one of the most challenging problems in computer vision. In this paper, we propose a new algorithm for static camera foreground segmentation. It combines Gaussian mixture model (GMM) and active contours method, and produces much better results than conventional background subtraction methods. It formulates foreground segmentation as an energy minimization problem and minimizes the energy function using curve evolution method. Because of the integration of GMM background model, shadow elimination term and curve evolution edge stopping term into energy function, it achieves more accurate segmentation than existing method of the same type. Promising results on real images demonstrate the potential of the presented method.
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