Robust object tracking in crowd dynamic scenes using explicit stereo depth

Chi Li, Le Lu, Gregory D. Hager, Jianyu Tang, Hanzi Wang

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

Abstract

In this paper, we exploit robust depth information with simple color-shape appearance model on single object tracking in crowd dynamic scenes. Since binocular video streams are captured from a moving camera rig, background subtraction cannot provide a reliable enhancement of region of interest. Our main contribution is a novel tracking strategy to employ explicit stereo depth to track and segment object in crowd dynamic scenes with occlusion handling. Appearance cues including color and shape play a secondary role to further extract the foreground acquired by previous depth-based segmentation. The proposed depth-driven tracking approach can largely alleviate the drifting issue, especially when the object frequently interacts with similar background in long sequence tracking. The problems caused by rapid object appearance change can also be avoided due to the stability of the depth cue. Furthermore, we propose a new, yet simple and effective depth-based scheme to cope with complete occlusion in tracking. From experiments on a large collection of challenging outdoor and indoor sequences, our algorithm demonstrates accurate and reliable tracking performance which outperforms other state-of-the-art competing algorithms.

Original languageEnglish (US)
Title of host publicationComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
PublisherSpringer Verlag
Pages71-85
Number of pages15
EditionPART 3
ISBN (Print)9783642374302
DOIs
StatePublished - Jan 1 2013
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: Nov 5 2012Nov 9 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume7726 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of
CityDaejeon
Period11/5/1211/9/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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