The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Joint Deep Learning for Pedestrian Detection
Abstract
Feature extraction, deformation handling, occlusion handling, and classification are four important components in pedestrian detection. Existing methods learn or design these components either individually or sequentially. The interaction among these components is not yet well explored. This paper proposes that they should be jointly learned in order to maximize their strengths through cooperation. We formulate these four components into a joint deep learning framework and propose a new deep network architecture. By establishing automatic, mutual interaction among components, the deep model achieves a 9% reduction in the average miss rate compared with the current best-performing pedestrian detection approaches on the largest Caltech benchmark dataset.
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Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving
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Metadata
- Authors
- Ouyang, W., & Wang, X.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 2056-2063
- OpenAlex
- W2156547346
- Cited by
- 660
- References
- 9
- FWCI
- 34.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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