Cited by 414Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsHuman Pose and Action Recognition

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Adaptive NMS: Refining Pedestrian Detection in a Crowd

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Liu, Songtao, Huang, Di, Wang, Yunhong·2019·2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

DOI 10.1109/cvpr.2019.00662

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Abstract

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density; (2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors; and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks.

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Authors: Liu, S., Huang, D., & Wang, Y.
Affiliations: State Key Laboratory of Software Development Environment (CN)
State Key Laboratory of Virtual Reality Technology and Systems (CN)
ORCID: Liu, Songtao
Huang, Di
Wang, Yunhong
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher: IEEE
Published: 2019-06
Pages: 6452-6461
ISSN: 1063-6919 (ISSN-L)
DOI: 10.1109/cvpr.2019.00662
OpenAlex: W2963769056
Cited by: 413
References: 52
Citations by year: 2026: 27 · 2025: 50 · 2024: 69 · 2023: 78 · 2022: 66 · 2021: 68 · 2020: 53 · 2019: 2
FWCI: 15.41
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: cc-by-nc-sa
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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, cc-by-nc-sa)
arXiv (Cornell University)
Funders: State Key Laboratory of Software Development Environment
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, non-maximum suppression, crowd density estimation, bounding box refinement, single-stage detectors, two-stage detection

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Adaptive NMS: Refining Pedestrian Detection in a Crowd

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