Upper Body Human Detection and Segmentation in Low Contrast Video

In the application of extracting human regions from videos, many existing methods may lose their efficacy when illumination varies or the human remains still. To address this problem, we propose a method in this paper for human region detection and segmentation by constructing a generalized human upper body model. The method mainly consists of two main procedures. First, foreground connected regions are extracted by background subtraction from the current frame and classified through a human upper body model pretrained with a support vector machine to determine whether they are human regions. Second, we assign an energy function to the region contour and apply an energy minimization procedure to evolve the contour when human regions are polluted by background; for example, a change in lighting conditions. After finding the optimal contour, we update the background and repeat the procedures in next frame. This feedback strategy rectifies the mistaken background regions promptly and extracts human regions correctly. Our experimental results demonstrate that the proposed method is robust enough to handle videos of low contrast as well as normal conditions.