Dense spatio-temporal motion segmentation for tracking multiple self-occluding people

Martin Hofmann, Gerhard Rigoll, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we describe a new dense spatio-temporal motion segmentation algorithm with application to tracking of people in crowded environments. The algorithm is based on state-of-the-art motion and image segmentation algorithms. We specifically make use of a mean shift image segmentation algorithm and two graph based motion segmentation algorithms. The resulting motion segmentation is on the one hand accurate and on the other hand computationally efficient. In addition our method is capable of handling mutual occlusions. This shows that motion segmentation can efficiently be used to simultaneously detect, track and segment moving objects. We apply this to tracking people in surveillance videos, but the algorithm is not limited to this class of scenes.

Original languageEnglish (US)
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
PublisherIEEE Computer Society
Pages9-14
Number of pages6
ISBN (Print)9781424470297
DOIs
StatePublished - 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 - San Francisco, CA, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

Name2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

Other

Other2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Country/TerritoryUnited States
CitySan Francisco, CA
Period6/13/106/18/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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