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Is Faster R-CNN Doing Well for Pedestrian Detection?

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Zhang, Liliang, Lin, Liang, Liang, Xiaodan, He, Kaiming·2016·Lecture Notes in Computer Science

DOI 10.1007/978-3-319-46475-6_28

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Authors: Zhang, L., Lin, L., Liang, X., & He, K.
Affiliations: Sun Yat-sen University (CN)
Microsoft Research Asia (China) (CN)
Corresponding author: Lin, Liang
ORCID: Lin, Liang
Liang, Xiaodan
He, Kaiming
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2016
Pages: 443-457
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-319-46475-6_28
OpenAlex: W2497039038
Cited by: 852
References: 35
Citations by year: 2026: 5 · 2025: 21 · 2024: 41 · 2023: 68 · 2022: 81 · 2021: 122 · 2020: 121 · 2019: 152 · 2018: 150 · 2017: 87 · 2016: 4
FWCI: 45.47
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Lecture notes in computer science (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Natural Science Foundation of China (2016YFB1001000)
Natural Science Foundation of Guangdong Province (S2013050014548)
National Natural Science Foundation of China-Guangdong Joint Fund
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 › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Keywords: pedestrian detection, Faster R-CNN, region proposal network, small object detection, hard negative mining, object detection

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