Adaptive human shape reconstruction via 3D head tracking for motion capture in changing environment

This paper describes a human shape reconstruction method from multiple cameras in daily living environment, which leads to robust markerless motion capture. Due to continual illumination changes in daily space, it had been difficult to get human shape by background subtraction methods. Recent statistical foreground segmentation techniques based on graph-cuts, which combine background subtraction information and image contrast, provide successful results; however, they fail to extract human shape when furniture such as tables and chairs are moved. In this paper, we focus on the results of face detectors that would be independent of such background changes and help to improve the robustness under movement of background objects. We propose a robust human shape reconstruction method with the following two characteristics. One is iterative image segmentation based on graph-cuts to integrate head position information into shape reconstruction. The other is high-precision head tracker to keep multi-view consistency. Experimental results show that proposed method has enhanced human pose estimation based on reconstructed human shape, and enables the system to deal with dynamic environment.