Markerless human body motion capture using Markov random field and dynamic graph cuts

Abstract

Current vision-based human body motion capture methods always use passive markers that are attached to key locations on the human body. However, such systems may confront subjects with cumbersome markers, making it difficult to convert the marker data into kinematic motion. In this paper, we propose a new algorithm for markerless computer vision-based human body motion capture. We compute volume data (voxels) representation from the images using the method of SFS (shape from silhouettes), and consider the volume data as a MRF (Markov random field). Then we match a predefined human body model with pose parameters to the volume data, and the calculation of this matching is transformed into energy function minimization. We convert the problem of energy function construction into a 3D graph construction, and get the minimal energy by the max-flow theory. Finally, we recover the human pose by Powell algorithm.