Tracking articulated bodies using Generalized Expectation Maximization
Andrea Fossati, Élise Arnaud, Radu Horaud, Pascal Fua · 2008
A generalized expectation maximization (GEM) algorithm is used to retrieve the pose of a person from a monocular video sequence shot with a moving camera. After embedding the set of possible poses in a low dimensional space using principal component analysis, the configuration that gives the best match to the input image is held as estimate for the current frame. This match is computed iterating GEM to assign edge pixels to the correct body part and to find the body pose that maximizes the likelihood of the assignments.