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Filtered channel features for pedestrian detection
Open accessAbstract
This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.
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Metadata
- Authors
- Zhang, S., Benenson, R., & Schiele, B.
- Affiliations
- Max Planck Institute for Informatics (DE)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 1751-1760
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2074777933
- Cited by
- 395
- References
- 45
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 9 · 2023: 9 · 2022: 24 · 2021: 29 · 2020: 43 · 2019: 58 · 2018: 73 · 2017: 78 · 2016: 45 · 2015: 15
- FWCI
- 28.85
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- pedestrian detection, low-level features, histogram of oriented gradients, optical flow features, feature fusion, KITTI dataset
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