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2013 IEEE Conference on Computer Vision and Pattern Recognition (2013)
Portland, OR, USA USA
June 23, 2013 to June 28, 2013
ISSN: 1063-6919
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-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.
INDEX TERMS
convolutional, deep learning, computer vision, pedestrian, detection, unsupervised
CITATION

S. Chintala, K. Kavukcuoglu, P. Sermanet and Y. Lecun, "Pedestrian Detection with Unsupervised Multi-stage Feature Learning," 2013 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Portland, OR, USA USA, 2013, pp. 3626-3633.
doi:10.1109/CVPR.2013.465
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