ML Anthology

Measurement Integration Under Inconsistency for Robust Tracking

Gang Hua, Ying Wu
CVPR 2006 pp. 650-657
doi:10.1109/CVPR.2006.181 /cvpr/2006/hua2006cvpr-measurement/

Abstract

The solutions to many vision problems involve integrating measurements from multiple sources. Most existing methods rely on a hidden assumption, i.e., these measurements are consistent. In reality, unfortunately, this may not hold. The fact that naively fusing inconsistent measurements amounts to failing these methods indicates that this is not a trivial problem. This paper presents a novel approach to handling it. A new theorem is proven that gives two algebraic criteria to examine the consistency and inconsistency. In addition, a more general criterion is presented. Based on the theoretical analysis, a new information integration method is proposed and leads to encouraging results when applied to the task of visual tracking.

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Cite

Text

Hua and Wu. "Measurement Integration Under Inconsistency for Robust Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.181

Markdown

[Hua and Wu. "Measurement Integration Under Inconsistency for Robust Tracking." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/hua2006cvpr-measurement/) doi:10.1109/CVPR.2006.181

BibTeX

@inproceedings{hua2006cvpr-measurement,
  title     = {{Measurement Integration Under Inconsistency for Robust Tracking}},
  author    = {Hua, Gang and Wu, Ying},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2006},
  pages     = {650-657},
  doi       = {10.1109/CVPR.2006.181},
  url       = {https://mlanthology.org/cvpr/2006/hua2006cvpr-measurement/}
}