ML Anthology

Fuzzy Chamfer Distance and Its Probabilistic Formulation for Visual Tracking

Yonggang Jin, Farzin Mokhtarian, Miroslaw Bober, John Illingworth
CVPR 2008
doi:10.1109/CVPR.2008.4587570 /cvpr/2008/jin2008cvpr-fuzzy/

Abstract

The paper presents a fuzzy chamfer distance and its probabilistic formulation for edge-based visual tracking. First, connections of the chamfer distance and the Hausdorff distance with fuzzy objective functions for clustering are shown using a reformulation theorem. A fuzzy chamfer distance (FCD) based on fuzzy objective functions and a probabilistic formulation of the fuzzy chamfer distance (PFCD) based on data association methods are then presented for tracking, which can all be regarded as reformulated fuzzy objective functions and minimized with iterative algorithms. Results on challenging sequences demonstrate the performance of the proposed tracking method.

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Cite

Text

Jin et al. "Fuzzy Chamfer Distance and Its Probabilistic Formulation for Visual Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587570

Markdown

[Jin et al. "Fuzzy Chamfer Distance and Its Probabilistic Formulation for Visual Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/jin2008cvpr-fuzzy/) doi:10.1109/CVPR.2008.4587570

BibTeX

@inproceedings{jin2008cvpr-fuzzy,
  title     = {{Fuzzy Chamfer Distance and Its Probabilistic Formulation for Visual Tracking}},
  author    = {Jin, Yonggang and Mokhtarian, Farzin and Bober, Miroslaw and Illingworth, John},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587570},
  url       = {https://mlanthology.org/cvpr/2008/jin2008cvpr-fuzzy/}
}