ML Anthology

An Efficient and Robust Human Classification Algorithm Using Finite Frequencies Probing

Yang Ran, Isaac Weiss, Qinfen Zheng, Larry S. Davis
CVPR 2004 pp. 132
doi:10.1109/CVPR.2004.299 /cvpr/2004/ran2004cvpr-efficient/

Abstract

This paper describes a periodicity motion detection based object classification algorithm for infrared videos. Given a detected and tracked object, the goal is to analyze the periodic signature of its motion pattern. We propose an efficient and robust solution, which is related to the frequency estimation in speech recognition. Periodic reference functions are correlated with the video signal. Experimental results for both infrared and visible videos acquired by ground-based as well as airborne moving sensors are presented.

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Cite

Text

Ran et al. "An Efficient and Robust Human Classification Algorithm Using Finite Frequencies Probing." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004. doi:10.1109/CVPR.2004.299

Markdown

[Ran et al. "An Efficient and Robust Human Classification Algorithm Using Finite Frequencies Probing." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2004.](https://mlanthology.org/cvpr/2004/ran2004cvpr-efficient/) doi:10.1109/CVPR.2004.299

BibTeX

@inproceedings{ran2004cvpr-efficient,
  title     = {{An Efficient and Robust Human Classification Algorithm Using Finite Frequencies Probing}},
  author    = {Ran, Yang and Weiss, Isaac and Zheng, Qinfen and Davis, Larry S.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2004},
  pages     = {132},
  doi       = {10.1109/CVPR.2004.299},
  url       = {https://mlanthology.org/cvpr/2004/ran2004cvpr-efficient/}
}