NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
Open accessAbstract
In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many convolutional neural networks (CNN) are designed for tackling this task. However, currently released datasets are so small-scale that they can not meet the needs of the supervised CNN-based algorithms. To remedy this problem, we construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range ( 0 ∼ 20,033). Besides, a benchmark website is developed for impartially evaluating the different methods, which allows researchers to submit the results of the test set. Based on the proposed dataset, we further describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data. What's more, the benchmark is deployed at https://www.crowdbenchmark.com/, and the dataset/code/models/results are available at https://gjy3035.github.io/NWPU-Crowd-Sample-Code/.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Draper, N. R., & Smith, H. (2005) · Technometrics · doi:10.1198/tech.2005.s303 · Cited by 17,993 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · see citing works
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · see citing works
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913 · see citing works
Draper, N. R., & Smith, H. (1998) · Wiley series in probability and statistics · doi:10.1002/9781118625590 · Cited by 5,086 · see citing works
Gunst, R. F., Draper, N. R., & Smith, H. C. (1999) · Technometrics · doi:10.2307/1270576 · Cited by 3,795 · see citing works
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.70 · Cited by 2,356 · see citing works
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
Li, Y., Zhang, X., & Chen, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00120 · Cited by 1,794 · see citing works
The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Neuhold, G., Ollmann, T., Bulo, S. R., & Kontschieder, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.534 · Cited by 1,434 · see citing works
Privacy preserving crowd monitoring: Counting people without people models or tracking
Chan, A. B., Liang, J., & Vasconcelos, N. M. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587569 · Cited by 1,198 · see citing works
Multi-source Multi-scale Counting in Extremely Dense Crowd Images
Idrees, H., Saleemi, I., Seibert, C., & Shah, M. A. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.329 · Cited by 1,065 · see citing works
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S. A., Rajpoot, N., & Shah, M. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_33 · Cited by 891 · see citing works
Liu, W., Salzmann, M., & Fua, P. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00524 · Cited by 782 · see citing works
Scale Aggregation Network for Accurate and Efficient Crowd Counting
Cao, X., Wang, Z., Zhao, Y., & Su, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_45 · Cited by 774 · see citing works
Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs
Sindagi, V. A., & Patel, V. M. (2017) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.206 · Cited by 734 · see citing works
Hu, P., & Ramanan, D. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.166 · Cited by 709 · see citing works
Feature Mining for Localised Crowd Counting
Chen, K., Loy, C. C., Gong, S., & Xiang, T. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.21 · Cited by 691 · see citing works
Bayesian Loss for Crowd Count Estimation With Point Supervision
Ma, Z., Wei, X., Hong, X., & Gong, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00624 · Cited by 668 · see citing works
Learning From Synthetic Data for Crowd Counting in the Wild
Wang, Q., Gao, J., Lin, W., & Yuan, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00839 · Cited by 654 · see citing works
Self-citationA Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis
Ali, S., & Shah, M. A. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.382977 · Cited by 594 · see citing works
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
Liu, J., Gao, C., Meng, D., & Hauptmann, A. G. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00545 · Cited by 423 · see citing works
Crowd Counting via Adversarial Cross-Scale Consistency Pursuit
Shen, Z., Xu, Y., Ni, B., Wang, M., Hu, J., & Yang, X. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00550 · Cited by 379 · see citing works
Ranjan, V., Le, H., & Hoai, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_17 · Cited by 323 · see citing works
Crowd Counting With Deep Structured Scale Integration Network
Liu, L., Qiu, Z., Li, G., Liu, S., Ouyang, W., & Lin, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00186 · Cited by 302 · see citing works
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method
Sindagi, V., Yasarla, R., & Patel, V. M. M. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3035969 · Cited by 282 · see citing works
Crowd Counting via Scale-Adaptive Convolutional Neural Network
Zhang, L., Shi, M., & Chen, Q. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00127 · Cited by 281 · see citing works
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Gao, J., Wang, Q., & Li, X. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2919139 · Cited by 279 · see citing works
Self-citationSCAR: Spatial-/channel-wise attention regression networks for crowd counting
Gao, J., Wang, Q., & Yuan, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.018 · Cited by 227 · see citing works
Self-citationDensity Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization
Lian, D., Li, J., Zheng, J., Luo, W., & Gao, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00192 · Cited by 224 · see citing works
Perspective-Guided Convolution Networks for Crowd Counting
Yan, Z., Yuan, Y., Zuo, W., et al. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00104 · Cited by 205 · see citing works
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization
Liu, C., Weng, X., & Mu, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00131 · Cited by 199 · see citing works
Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank
Liu, X., van de Weijer, J., & Bagdanov, A. D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2899857 · Cited by 187 · see citing works
Adaptive Density Map Generation for Crowd Counting
Wan, J., & Chan, A. B. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00122 · Cited by 183 · see citing works
Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs
Zhang, Q., & Chan, A. B. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00849 · Cited by 159 · see citing works
Locality-Constrained Spatial Transformer Network for Video Crowd Counting
Fang, Y., Zhan, B., Cai, W., Gao, S., & Hu, B. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00145 · Cited by 130 · see citing works
Nonlinear Regression via Deep Negative Correlation Learning
Zhang, L., Shi, Z., Cheng, M., Liu, Y., Bian, J.-W., Zhou, J. T., Guoyan Zheng, A., & Zeng, Z. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2943860 · Cited by 129 · see citing works
Residual Regression With Semantic Prior for Crowd Counting
Wan, J., Luo, W., Wu, B., et al. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00416 · Cited by 122 · see citing works
Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset
Zhang, C., Kang, K., Li, H., Wang, X., Xie, R., & Yang, X. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2542585 · Cited by 118 · see citing works
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
Sindagi, V. A., Yasarla, R., & Patel, V. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00131 · Cited by 112 · see citing works
Switching Convolutional Neural Network for Crowd Counting
Sam, D. B., Surya, S., & Babu, R. V. (2017) · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · doi:10.48550/arxiv.1708.00199 · Cited by 99 · see citing works
Composition Loss for Counting, Density Map Estimation and Localization in Dense CrowdsPreprint
Idrees, H., Tayyab, M., Athrey, K., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.01050 · Cited by 61 · see citing works
The Solution Path Algorithm for Identity-Aware Multi-object Tracking
Yu, S.-I., Meng, D., Zuo, W., & Hauptmann, A. G. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.420 · Cited by 39 · see citing works
C^3 Framework: An Open-source PyTorch Code for Crowd CountingPreprint
Gao, J., Lin, W., Zhao, B. L., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02724 · Cited by 34 · see citing works
Self-citationGao, J., Han, T., Wang, Q., & Yuan, Y. (2019) · arXiv (Cornell University) · Cited by 30 · see citing works
Self-citationWen, L., Du, D., Zhu, P., Hu, Q., Wang, Q., Bo, L., & Lyu, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.01811 · Cited by 25 · see citing works
Self-citationIterative Crowd CountingPreprint
Ranjan, V., Lê, H., & Hoai, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09959 · Cited by 24 · see citing works
MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground ImageryPreprint
Bahmanyar, R., Vig, E., & Reinartz, P. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.12743 · Cited by 22 · see citing works
A Deeply-Recursive Convolutional Network for Crowd CountingPreprint
Ding, X., Lin, Z., He, F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.05633 · Cited by 16 · see citing works
Domain-adaptive Crowd Counting via High-quality Image Translation and Density ReconstructionPreprint
Gao, J., Han, T., Wang, Q., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03677 · Cited by 7 · see citing works
Self-citationJHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark MethodPreprint
Sindagi, V. A., Yasarla, R., & Patel, V. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03597 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [38][38] W. Liu, M. Salzmann, and P. Fua, 'Context-aware crowd counting,' in Proc. Conf. Comput. Vis. Pattern Recognit. , 2019, pp. 5099-5108. -
Cited by
Sorted by citations
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
- Authors
- Wang, Q., Gao, J., Lin, W., & Li, X.
- Affiliations
- Northwestern Polytechnical University (CN)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021-06-01
- Volume
- 43
- Issue
- 6
- Pages
- 2141-2149
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3027606690
- Cited by
- 510
- References
- 59
- Citations by year
- 2026: 63 · 2025: 80 · 2024: 93 · 2023: 81 · 2022: 95 · 2021: 77 · 2020: 19 · 2019: 2
- FWCI
- 26.43
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research and Development Program of China (2017YFB1002202)
- National Natural Science Foundation of China (U1864204, 61773316, U1801262, 61871470)
- 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 › Face recognition and analysis
- Keywords
- crowd counting, crowd localization, large-scale datasets, head detection, crowd monitoring, illumination variation, crowd density
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
