Extended lucas-kanade tracking Conference Paper uri icon


  • 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 …

publication date

  • January 1, 2014