Pedestrian Detection with Unsupervised Multi-stage Feature Learning

Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala, Yann Lecun; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 3626-3633

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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[bibtex]
@InProceedings{Sermanet_2013_CVPR,
author = {Sermanet, Pierre and Kavukcuoglu, Koray and Chintala, Soumith and Lecun, Yann},
title = {Pedestrian Detection with Unsupervised Multi-stage Feature Learning},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}