Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala, Yann Lecun

CVPR 2013

doi:10.1109/CVPR.2013.465 /cvpr/2013/sermanet2013cvpr-pedestrian/

Abstract

Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-theart and competitive results on all major pedestrian datasets with a convolutional network model. The model uses a few new twists, such as multi-stage features, connections that skip layers to integrate global shape information with local distinctive motif information, and an unsupervised method based on convolutional sparse coding to pre-train the filters at each stage.

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Cite

Text

Sermanet et al. "Pedestrian Detection with Unsupervised Multi-Stage Feature Learning." Conference on Computer Vision and Pattern Recognition, 2013. doi:10.1109/CVPR.2013.465

Markdown

[Sermanet et al. "Pedestrian Detection with Unsupervised Multi-Stage Feature Learning." Conference on Computer Vision and Pattern Recognition, 2013.](https://mlanthology.org/cvpr/2013/sermanet2013cvpr-pedestrian/) doi:10.1109/CVPR.2013.465

BibTeX

@inproceedings{sermanet2013cvpr-pedestrian,
  title     = {{Pedestrian Detection with Unsupervised Multi-Stage Feature Learning}},
  author    = {Sermanet, Pierre and Kavukcuoglu, Koray and Chintala, Soumith and Lecun, Yann},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2013},
  doi       = {10.1109/CVPR.2013.465},
  url       = {https://mlanthology.org/cvpr/2013/sermanet2013cvpr-pedestrian/}
}