PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Markerless Full Body Tracking by Integrating Multiple Cues
Roland Kehl, Matthieu Bray and Luc Van Gool
In: PHI'05 Workshop in Conjunction with ICCV 2005, Beijing, China(2005).


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.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
ID Code:1757
Deposited By:Roland Kehl
Deposited On:28 November 2005