A generic approach to simultaneous tracking and verification in video

Baoxin Li, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


In this paper, a generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density 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 a measurement vector Z and two hypotheses H 1 and H 0, we first estimate posterior probabilities P(H 0|Z) and P(H 1|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.

Original languageEnglish (US)
Pages (from-to)530-544
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number5
StatePublished - May 2002
Externally publishedYes


  • Importance sampling
  • Monte Carlo method
  • Object verification
  • Visual tracking

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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