Abstract

We present a novel approach to modelling the non-linear and time- varying dynamics of human motion, using statistical methods to capture the char- acteristic motion patterns that exist in typical human activities. Our method is based on automatically clustering the body pose space into connected regions ex- hibiting similar dynamical characteristics, modelling the dynamics in each region as a Gaussian autoregressive process. Activities that would require large numbers of exemplars in example based methods are covered by comparatively few motion models. Different regions correspond roughly to different action-fragments and our class inference scheme allows for smooth transitions between these, thus mak- ing it useful for activity recognition tasks. The method is used to track activities including walking, running, etc., using a planar 2D body model. Its effectiveness is demonstrated by its success in tracking complicated motions like turns, without any key frames or 3D information.