Pedestrian detection with unsupervised multi-stage feature learning

Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala, Yann Lecun

Research output: Contribution to journalConference article

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)
Article number6619309
Pages (from-to)3626-3633
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
StatePublished - 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: Jun 23 2013Jun 28 2013

Keywords

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

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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