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Pedestrian Detection with Unsupervised Multi-stage Feature Learning
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.
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Metadata
- Authors
- Sermanet, P., Kavukcuoglu, K., Chintala, S., & Lecun, Y.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 3626-3633
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2162741153
- Cited by
- 742
- References
- 9
- FWCI
- 56.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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