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Contour cue based particle filter for monocular human motion tracking

  • Hanlu Li*
  • , Zhong Zhou
  • , Shujun Zhang
  • , Wei Wu
  • *Corresponding author for this work
  • Beihang University
  • Qingdao University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Particle filter is widely used in human motion tracking but its efficiency is low. A contour cue based particle filter algorithm is proposed in this paper for the human motion tracking in a mark-erless monocular video. The likelihood of sampled particles is measured by chamfer distance between two contours. One con-tour is extracted from the video image. The other is transformed from the sampled particle. The value of likelihood is the weight of corresponding particle. Then weighted particles are optimized by Levenberg-Marquardt method to make the final estimation closer to the posterior distribution of motion state. Apart from this, the skin part of human body is detected to constrain the sampled particles when the contour feature points are not sufficient with large occlusion. The experiment result shows that the contour cue based method is more efficient than the edge method.

Original languageEnglish
Title of host publicationProceedings - VRCAI 2010, ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Application to Industry
Pages151-154
Number of pages4
DOIs
StatePublished - 2010
Event9th ACM SIGGRAPH International Conference on VR Continuum and Its Applications in Industry, VRCAI 2010 - Seoul, Korea, Republic of
Duration: 12 Dec 201013 Dec 2010

Publication series

NameProceedings - VRCAI 2010, ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Application to Industry

Conference

Conference9th ACM SIGGRAPH International Conference on VR Continuum and Its Applications in Industry, VRCAI 2010
Country/TerritoryKorea, Republic of
CitySeoul
Period12/12/1013/12/10

Keywords

  • Contour cue
  • Estimation
  • Human motion tracking
  • Likelihood measurement
  • Particle filter

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