Human movement tracking and analysis with Kalman filtering and global optimization techniques

Abstract

This paper addresses the problem of tracking feature points along image sequences to analyze the undergoing human movement. An approach based on Kalman filtering performs the estimation and correction of the feature point's movement in every image frame, and optimizes the incorporation of the measured data in order to establish the best global correspondence. We propose a criterion to establish correspondences between the group of estimates in each image and the new data to include, using an optimization criterion that minimizes the global matching cost based on the Mahalanobis distance. With this solution, we are also able to deal with the occlusion and appearance of feature points along with the movement tracking. In this paper we present experimental results obtained on real human tracking images that validate our approach.