ML Anthology

Pedestrian Detection Using Wavelet Templates

Michael Oren, Constantine Papageorgiou, Pawan Sinha, Edgar Osuna, Tomaso A. Poggio
CVPR 1997 pp. 193-199
doi:10.1109/CVPR.1997.609319 /cvpr/1997/oren1997cvpr-pedestrian/

Abstract

This paper presents a trainable object detection architecture that is applied to detecting people in static images of cluttered scenes. This problem poses several challenges. People are highly non-rigid objects with a high degree of variability in size, shape, color, and texture. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or on motion. The detection technique is based on the novel idea of the wavelet template that defines the shape of an object in terms of a subset of the wavelet coefficients of the image. It is invariant to changes in color and texture and can be used to robustly define a rich and complex class of objects such as people. We show how the invariant properties and computational efficiency of the wavelet template make it an effective tool for object detection.

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Cite

Text

Oren et al. "Pedestrian Detection Using Wavelet Templates." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997. doi:10.1109/CVPR.1997.609319

Markdown

[Oren et al. "Pedestrian Detection Using Wavelet Templates." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1997.](https://mlanthology.org/cvpr/1997/oren1997cvpr-pedestrian/) doi:10.1109/CVPR.1997.609319

BibTeX

@inproceedings{oren1997cvpr-pedestrian,
  title     = {{Pedestrian Detection Using Wavelet Templates}},
  author    = {Oren, Michael and Papageorgiou, Constantine and Sinha, Pawan and Osuna, Edgar and Poggio, Tomaso A.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1997},
  pages     = {193-199},
  doi       = {10.1109/CVPR.1997.609319},
  url       = {https://mlanthology.org/cvpr/1997/oren1997cvpr-pedestrian/}
}