ML Anthology

Visual Tracking via Geometric Particle Filtering on the Affine Group with Optimal Importance Functions

Junghyun Kwon, Kyoung Mu Lee, Frank Chongwoo Park
CVPR 2009 pp. 991-998
doi:10.1109/CVPR.2009.5206501 /cvpr/2009/kwon2009cvpr-visual/

Abstract

We propose a geometric method for visual tracking, in which the 2-D affine motion of a given object template is estimated in a video sequence by means of coordinate-invariant particle filtering on the 2-D affine group Aff(2). Tracking performance is further enhanced through a geometrically defined optimal importance function, obtained explicitly via Taylor expansion of a principal component analysis based measurement function on Aff(2). The efficiency of our approach to tracking is demonstrated via comparative experiments.

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Cite

Text

Kwon et al. "Visual Tracking via Geometric Particle Filtering on the Affine Group with Optimal Importance Functions." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009. doi:10.1109/CVPR.2009.5206501

Markdown

[Kwon et al. "Visual Tracking via Geometric Particle Filtering on the Affine Group with Optimal Importance Functions." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2009.](https://mlanthology.org/cvpr/2009/kwon2009cvpr-visual/) doi:10.1109/CVPR.2009.5206501

BibTeX

@inproceedings{kwon2009cvpr-visual,
  title     = {{Visual Tracking via Geometric Particle Filtering on the Affine Group with Optimal Importance Functions}},
  author    = {Kwon, Junghyun and Lee, Kyoung Mu and Park, Frank Chongwoo},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2009},
  pages     = {991-998},
  doi       = {10.1109/CVPR.2009.5206501},
  url       = {https://mlanthology.org/cvpr/2009/kwon2009cvpr-visual/}
}