Pedestrian detection with unsupervised multi-stage feature learning

Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala, Yann LeCun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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-the-art 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.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Pages3626-3633
Number of pages8
DOIs
StatePublished - 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013

Other

Other26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013
CountryUnited States
CityPortland, OR
Period6/23/136/28/13

Fingerprint

Deep learning

Keywords

  • computer vision
  • convolutional
  • deep learning
  • detection
  • pedestrian
  • unsupervised

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Sermanet, P., Kavukcuoglu, K., Chintala, S., & LeCun, Y. (2013). Pedestrian detection with unsupervised multi-stage feature learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 3626-3633). [6619309] https://doi.org/10.1109/CVPR.2013.465

Pedestrian detection with unsupervised multi-stage feature learning. / Sermanet, Pierre; Kavukcuoglu, Koray; Chintala, Soumith; LeCun, Yann.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013. p. 3626-3633 6619309.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sermanet, P, Kavukcuoglu, K, Chintala, S & LeCun, Y 2013, Pedestrian detection with unsupervised multi-stage feature learning. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., 6619309, pp. 3626-3633, 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, Portland, OR, United States, 6/23/13. https://doi.org/10.1109/CVPR.2013.465
Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y. Pedestrian detection with unsupervised multi-stage feature learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013. p. 3626-3633. 6619309 https://doi.org/10.1109/CVPR.2013.465
Sermanet, Pierre ; Kavukcuoglu, Koray ; Chintala, Soumith ; LeCun, Yann. / Pedestrian detection with unsupervised multi-stage feature learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2013. pp. 3626-3633
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