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

Yaming Wang, George Baciu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)315-325
Number of pages11
JournalPattern Recognition Letters
Volume24
Issue number1-3
DOIs
Publication statusPublished - 1 Jan 2003

Keywords

  • Cross-entropy
  • Human motion
  • Monocular image sequence
  • Regularization
  • Relative deformation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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