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Extended Lucas-Kanade Tracking

  • Shaul Oron
  • Aharon Bar-Hille
  • Shai Avidan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)

Abstract

The Lucas-Kanade (LK) method is a classic tracking algorithm exploiting target structural constraints thorough template matching. Extended Lucas Kanade or ELK casts the original LK algorithm as a maximum likelihood optimization and then extends it by considering pixel object / background likelihoods in the optimization. Template matching and pixel-based object / background segregation are tied together by a unified Bayesian framework. In this framework two log-likelihood terms related to pixel object / background affiliation are introduced in addition to the standard LK template matching term. Tracking is performed using an EM algorithm, in which the E-step corresponds to pixel object/background inference, and the M-step to parameter optimization. The final algorithm, implemented using a classifier for object / background modeling and equipped with simple template update and occlusion handling logic, is evaluated on two challenging data-sets containing 50 sequences each. The first is a recently published benchmark where ELK ranks 3rd among 30 tracking methods evaluated. On the second data-set of vehicles undergoing severe view point changes ELK ranks in 1st place outperforming state-of-the-art methods.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shaul Oron
    • 1
  • Aharon Bar-Hille
    • 2
  • Shai Avidan
    • 1
  1. 1.Tel Aviv UniversityIsrael
  2. 2.Microsoft ResearchUSA

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