Extended Lucas-Kanade Tracking
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.
Cite
Text
Oron et al. "Extended Lucas-Kanade Tracking." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10602-1_10Markdown
[Oron et al. "Extended Lucas-Kanade Tracking." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/oron2014eccv-extended/) doi:10.1007/978-3-319-10602-1_10BibTeX
@inproceedings{oron2014eccv-extended,
title = {{Extended Lucas-Kanade Tracking}},
author = {Oron, Shaul and Bar-Hillel, Aharon and Avidan, Shai},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {142-156},
doi = {10.1007/978-3-319-10602-1_10},
url = {https://mlanthology.org/eccv/2014/oron2014eccv-extended/}
}