Markerless Full Body Tracking by Integrating Multiple Cues
This work proposes a novel markerless solution to full body pose tracking by integrating multiple cues such as edges, color information and volumetric reconstruction. A model built from superellipsoids is fitted to a colored volumetric reconstruction by using Stochastic Meta Descent (SMD) while taking advantage of the color information to overcome ambiguities caused by limbs touching each other. As the volumetric reconstruction is inaccurate at times, the tracking is refined by matching model contours against image edges. The model consists of a set of superellipsoids. For the matching, we introduce a way to efficiently render the contours of superellipsoids, incl. self-occlusions. The integration of edge information is demonstrated to increase the robustness and accuracy of the tracking. Several challenging body tracking sequences, showing complex movements and full articulation, illustrate this.