Classifying Human Dynamics Without Contact Forces UCLA CSD-TR 050045
We develop a classification algorithm for hybrid autoregressive models of human motion for the purpose of video-based analysis and recognition. We assume that some temporal statistics are extracted from the images, and we use them to infer a dynamical system that explicitly models contact forces. We then develop a distance between such models that explicitly factors out exogenous inputs that are not unique to an individual or her gait. We show that such a distance is far more discriminative than the distance between simple linear systems, where most of the energy is devoted to modeling the dynamics of spurious nuisances such as contact forces.