Joint probabilistic techniques for tracking multi-part objects

Christopher Rasmussen, Gregory D. Hager

Research output: Contribution to journalConference article

Abstract

Common objects such as people and cars comprise many visual parts and attributes, yet image-based tracking algorithms are often keyed to only one of a target's identifying characteristics. In this paper, we present a framework for combining and sharing information among several state estimation processes operating on the same underlying visual object. Well-known techniques for joint probabilistic data association are adapted to yield increased robustness when multiple trackers attuned to disparate visual cues are deployed simultaneously. We also formulate a measure of tracker confidence, based on distinctiveness and occlusion probability, which permits the deactivation of trackers before erroneous state estimates adversely affect the ensemble. We will discuss experiments focusing on color-region- and snake-based tracking that demonstrate the efficacy of this approach.

Original languageEnglish (US)
Pages (from-to)16-21
Number of pages6
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - Dec 1 1998
EventProceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Santa Barbara, CA, USA
Duration: Jun 23 1998Jun 25 1998

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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