Robust Human Pose Recognition Using Unlabelled Markers

In this paper, we tackle robust human pose recognition using unlabelled markers obtained from an optical marker-based motion capture system. A coarse-to-fine fast pose matching algorithm is presented with the following three steps. Given a query pose, firstly, the majority of the non-matching poses are rejected according to marker distributions along the radius and height dimensions. Secondly, relative rotation angles between the query pose and the remaining candidate poses are estimated using a fast histogram matching method based on circular convolution implemented using the fast Fourier transform. Finally, rotation angle estimates are refined using nonlinear least square minimization through the Levenberg-Marquardt minimization. In the presence of multiple solutions, false poses can be effectively removed by thresholding the minimized matching scores. The proposed framework can handle missing markers caused by occlusion. Experimental results using real motion capture data show the efficacy of the proposed approach.