Adaptive NMS: Refining Pedestrian Detection in a Crowd
Open accessAbstract
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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Many works have been proposed to solve the problems of greedy selection.
“Many works have been proposed to solve the problems mentioned above [107, 112, 114, 115].”
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You Should Look at All Objects
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Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings
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An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians
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Pedestrian Detection by Exemplar-Guided Contrastive Learning
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OAF-Net: An Occlusion-Aware Anchor-Free Network for Pedestrian Detection in a Crowd
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[Retracted] A Review of Intelligent Driving Pedestrian Detection Based on Deep Learning
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Toward Effective Traffic Sign Detection via Two-Stage Fusion Neural Networks
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Infrared Object Detection Method Based on DBD-YOLOv8
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Object Detection, Recognition, and Tracking Algorithms for ADASs—A Study on Recent Trends
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Multi-Pedestrian Tracking with Clusters
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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)
- 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)
- 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
- 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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