Visual tracking using the sum of conditional variance

Rogério Richa, Raphael Sznitman, Russell Taylor, Gregory Hager

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

Abstract

The goal of this paper is to introduce a direct visual tracking method based on an image similarity measure called the sum of conditional variance (SCV). The SCV was originally proposed in the medical imaging domain for registering multi-modal images. In the context of visual tracking, the SCV is invariant to non-linear illumination variations, multi-modal and computationally inexpensive. Compared to information theoretic tracking methods, it requires less iterations to converge and has a significantly larger convergence radius. The novelty in this paper is a generalization of the efficient second-order minimization formulation for tracking using the SCV, allowing us to combine the efficient second-order approximation of the Hessian with a similarity metric invariant to non-linear illumination variations. The result is a visual tracking method that copes with non-linear illumination variations without requiring the estimation of photometric correction parameters at every iteration. We demonstrate the superior performance of the proposed method through comparative studies and tracking experiments under challenging illumination conditions and rapid motions.

Original languageEnglish (US)
Title of host publicationIROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Subtitle of host publicationCelebrating 50 Years of Robotics
Pages2953-2958
Number of pages6
DOIs
StatePublished - Dec 29 2011
Event2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11 - San Francisco, CA, United States
Duration: Sep 25 2011Sep 30 2011

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
CountryUnited States
CitySan Francisco, CA
Period9/25/119/30/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Richa, R., Sznitman, R., Taylor, R., & Hager, G. (2011). Visual tracking using the sum of conditional variance. In IROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics (pp. 2953-2958). [6048295] (IEEE International Conference on Intelligent Robots and Systems). https://doi.org/10.1109/IROS.2011.6048295