In this paper we describe a system for automatic gait analysis. Different kinds of human gait are recognized using sequences of grey-level images. No markers are needed to get the trajectories of different body parts. The tracking of body parts and the classification are based on statistical models. We model several body parts and the background as mixture densities. The positions are determined iteratively, we begin with the most stable part to find. The anatomy of a human body restricts the area to search for the next one. From the trajectories, features for gait analysis are derived. These are used to train hidden Markov models (HMMs), one HMM for each kind of gait.
Meyer REF , one of the pioneers, has proposed a method for modeling several body parts and treating the background as mixture densities.
Gait Classification with HMMs for Trajectories of Body Parts Extracted by Mixture Densities
BMVC
Computer Science