Simultaneous tracking and verification via sequential posterior estimation

Baoxin Li, Rama Chellappa

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

37 Scopus citations

Abstract

An approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given measurement Z and two hypothesis H 1 and H 0, we first estimate posterior probabilities P(H 0|Z) and P(H 1|Z); and choose the one with the larger posterior probability as the true hypothesis. Applications of the approach are illustrated with experiments devised to evaluate the performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Pages110-117
Number of pages8
Volume2
StatePublished - 2000
Externally publishedYes
EventCVPR '2000: IEEE Conference on Computer Vision and Pattern Recognition - Hilton Head Island, SC, USA
Duration: Jun 13 2000Jun 15 2000

Other

OtherCVPR '2000: IEEE Conference on Computer Vision and Pattern Recognition
CityHilton Head Island, SC, USA
Period6/13/006/15/00

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Software
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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