The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
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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