An Accelerated Human Motion Tracking System Based on Voxel Reconstruction under Complex Environments

In this paper, we propose an automated and markless human motion tracking system, including voxel acquisition and motion tracking. We first explore the problem of voxel reconstruction under a complex environment. Specifically, the procedure of the voxel acquisition is conducted under cluttered background, which makes the high quality silhouette unavailable. An accelerated Bayesian sensor fusion framework combining the information of pixel and super-pixel is adopted to calculate the probability of voxel occupancy, which is achieved by focusing the computation on the image region of interest. The evaluation of reconstruction result is given as well. After the acquisition of voxels, we adopt a hierarchical optimization strategy to solve the problem of human motion tracking in a high-dimensional space. Finally, the performance of our human motion tracking system is compared with the ground truth from a commercial marker motion capture. The experimental results show the proposed human motion tracking system works well under a complex environment.