Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18029
Title: Human motion estimation from monocular image sequence based on cross-entropy regularization
Authors: Wang, Y
Baciu, G 
Keywords: Cross-entropy
Human motion
Monocular image sequence
Regularization
Relative deformation
Issue Date: 2003
Publisher: North-Holland
Source: Pattern recognition letters, 2003, v. 24, no. 1-3, p. 315-325 How to cite?
Journal: Pattern recognition letters 
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.
URI: http://hdl.handle.net/10397/18029
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/S0167-8655(02)00245-3
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

16
Last Week
0
Last month
0
Citations as of Oct 22, 2017

WEB OF SCIENCETM
Citations

11
Last Week
0
Last month
0
Citations as of Oct 24, 2017

Page view(s)

40
Last Week
1
Last month
Checked on Oct 23, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.