Human motion reconstruction from monocular images using genetic algorithms

Abstract

This paper proposed an optimization approach for human motion recovery from the uncalibrated monocular images containing unlimited human movements. A 3D skeleton human model based on anatomy knowledge is employed with encoded biomechanical constraints for the joints. Energy Function is defined to represent the deviations between projection features and extracted image features. Reconstruction procedure is developed to adjust joints and segments of the human body into their proper positions. Genetic Algorithms are adopted to find the optimal solution effectively in the high dimensional parameter space by simultaneously considering all the parameters of the human model. The experimental results are analysed by Deviation Penalty. Copyright © 2004 John Wiley & Sons, Ltd.