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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
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.
convolutional, deep learning, computer vision, pedestrian, detection, unsupervised

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.
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