Human motion estimation from monocular image sequence based on cross-entropy regularization

  • Authors:

  • Yaming Wang;George Baciu

  • Affiliations:

  • School of Informatics and Electronics, Zhejiang Institute of Science and Technology, 118 Wenyi Road, Hangzhou 310 033, China;GAMA Laboratory, Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2003

Quantified Score

Hi-index 0.10

Visualization

Abstract

Human motion estimation is crucial for many important applications. In this paper, a novel approach to human motion estimation from monocular image sequence is proposed. First, a non-rigid motion model called relative deformation model is developed. This model is based on the notion of relative deformation that introduces a new way for anthropomorphic body locomotion analysis including clinical gait analysis and robots motion analysis. Then, in order to deal with the ill-posed estimation problem, a regularization method based on Kullback's cross-entropy is proposed. By imposing the motion smoothness constraint, the entropy regularization converts the ill-posed problem into a well-posed one and guarantees the unique solution. Experimental results on image sequences of different walking men with different motion pattern demonstrate the feasibility of the proposed approach.