Human body gesture recognition using adapted auxiliary particle filtering

In this paper we propose a tracking scheme specifically tailored for tracking human body parts in cluttered scenes. We model the background and the human skin using Gaussian mixture models and we combine these estimates to localize the features to be tracked. We further use these estimates to determine the pixels which belong to the background and those which belong to the subject's skin and we incorporate this information in the observation model of the used tracking scheme. For handling self-occlusion (i.e., when one body part occludes another), we incorporate the information about the direction of the observed motion into the propagation model of the used tracking scheme. We demonstrate that the proposed method outperforms the conventional condensation and auxiliary particle filtering when the hands and the head are the tracked body features. For the purposes of human body gesture recognition, we use a variant of the longest common subsequence algorithm (LCSS) in order to acquire a distance measure between the acquired trajectories and we use this measure in order to define new kernels for a relevance vector machine (RVM) classification scheme. We present results on real image sequences from a small database depicting people performing 15 aerobic exercises.