In this paper we present a robust and efficient shape matching approach for Marker-less Motion Capture. Extracted features such as contour, gradient orientations and the turning function of the shape are embedded in a 1-D string. We formulate shape matching as a Linear Assignment Problem and propose to use Dynamic Time Warping on the string representation of shapes to discard unlikely correspondences and thereby to reduce ambiguities and spurious local minima. Furthermore, the proposed cost matrix pruning results in robustness to scaling, rotation and topological changes and allows to greatly reduce the computational cost. We show that our approach can track fast human motions where standard articulated Iterative Closest Point algorithms fail. © 2011 Springer-Verlag.
CITATION STYLE
Pons-Moll, G., Leal-Taixé, L., Truong, T., & Rosenhahn, B. (2011). Efficient and robust shape matching for model based human motion capture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6835 LNCS, pp. 416–425). https://doi.org/10.1007/978-3-642-23123-0_42
Mendeley helps you to discover research relevant for your work.