Human shape reconstruction via graph cuts for voxel-based markerless motion capture in intelligent environment

In this paper, we propose a robust and real-time 3D human shape reconstruction method in daily life spaces to make practical voxel-based motion capture systems. Our algorithm extracts human silhouette and reconstructs human shape via volume intersection from multi view point images. The method presented in this paper is based on energy minimization via graph cuts, and its main features are: 1) to reduce the background subtraction errors caused by background clutter, 2) to have robustness for influences of shadows, 3) to segment the foreground region even if moving objects other than human. The precise human shape reconstructed by the method improves the accuracy of human pose estimation. Especially, 3) leads to enhance the range of application of the voxel-based human pose estimation. We demonstrate the effectiveness of our approach in terms of both quantitative and qualitative performance where strong shadows appear and moving objects are present in intelligent environment.