The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
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 mentioned above [107, 112, 114, 115].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [115]
Citations
Cites
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ImageNet Large Scale Visual Recognition Challenge
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Shatnawi, M., Elshebli, E., Ferenc, E., & Péter, F. (2026) · Remote Sensing · doi:10.3390/rs18132096
An Improved Pedestrian Detection Model Based on RT-DETR for Dense and Occluded Scenes
Zhang, Y., Wang, C. H., Weng, W., Zhang, Z., Cao, S., & Xiong, Y. (2026) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis70043.2026.11572114
Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction
Hou, X., Liu, M., Du, S., & Fan, Z. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3708265
Wu, D., Peng, J., Yu, S., Xu, K., Ge, Y., & Ma, C. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3711500
BID-Det: Bias-Aware and instance-discriminative detection for soccer scenarios
Li, J., & Wei, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01059-w
Wang, W., Zhu, D., & Liu, H. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3725067
Chen, Z., Wang, J., Li, Z., Bai, Y., Fan, J., Liu, S., Wang, A., Shi, L., & Wang, Z. (2026) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste69562.2026.11649599
Self-citationSimpore, F., & Olayemi, M. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--61004
Bektemyssova, G., Keresh, A., Nuralykyzy, S., Ziyada, M., Barlykbay, N., & Joldasbayev, S. (2026) · Scientific Journal of Astana IT University · doi:10.37943/tsyt4497
STG: Structured Topology of Gridpoints for Occluded Pedestrian Detection
Qiu, T., Shen, J., & Zuo, X. (2026) · Sensors · doi:10.3390/s26175496
Clustering of count data using nested multinomial Dirichlet finite mixture model and its extensions
Alkhawaja, F., Amayri, M., & Bouguila, N. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-026-01250-7
Wang, D., Xie, S., Li, Y., Zhu, L., Lin, Y., & Yuan, C. (2026) · Technologies · doi:10.3390/technologies14100644
LGNet: A Lightweight Box-Guided Distillation Framework for Tiny UAV Object Detection
Zhang, L., Wang, R., Gu, X., Qiu, H., & Huang, Y. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729261
Metadata
- 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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