3D POSE ESTIMATION FROM SILHOUETTES IN CYCLIC ACTIVITIES ENCODED BY A DENSE GAUSSIANS MIXTURE MODEL
S. Amin Dadgar, Jean‐Christophe Nebel, Dimitrios Makris · 2010
This paper presents a system for 3D Pose estimation of cyclic activities (e.g. walking, jogging). Principal Component Analysis is used to compress the high dimensional space of poses. Human activities are encoded by Hidden Markov Models, overlaid on Gaussian Mixture Models. A generative approach based on the Annealed Particle Filter is used to estimate poses from silhouettes derived by a monocular camera. Experimental results indicate the value of the proposed Dense Gaussian Mixture Model when initialised by a gait cycle. 1