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.

Current markerless model-based human body motion capture methods always aim at accurate human body model and reconstruction surface contour. Unfortunately, because of the factors such as loose clothing, image noise and background segmentation errors, the efforts of these methods get very limited effects. In this paper, we propose a new algorithm for markerless model-based human body motion capture which is robust to the inaccurate human body reconstruction caused by the factors mentioned above. We extracted a volume data (voxel) representation from silhouettes in multiple video images. In the consideration of the human body model, we construct an articulated model with a potential energy which emphasize the skeleton of the human body and is not sensitive to the outline details of the human body surface. Then, we fit the human body model to the volume data in an expectation-maximization framework and recover the pose of the human body.