Dynamic Classifier for Non-rigid Human motion analysis

Automatic analysis (parsing) of non-rigid human motion in a cluttered outdoor enviroment is a useful but challenging task. In a single view point, the lack of depth order relations causes a major ambiguity of the object identities. Coupled with the non-rigidity of articulation, 3D human motion tracking/pose estimation in one view is a formidable problem. In this paper, we present a novel solution that directly address this depth ambiguity, in which we extend a discriminative analysis (Support Vector Machine (SVM)) to non-rigid human motion classification with a temporal generative motion model (Hidden Markov Model (HMM)). This method can discriminate dynamic depth ordering as well as 3D articulated motion automatically from 2D images. Experiments with this method have demonstrated promising results.