{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nwpu-crowd-a-large-scale-benchmark-for-crowd","title":"NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization","arxiv_id":"2001.03360","date":"2020-01-10","proceeding":null,"authors":["Qi. Wang","Junyu. Gao","Wei. Lin","Xuelong. Li"],"abstract":"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 \\url{https://www.crowdbenchmark.com/}, and the dataset/code/models/results are available at \\url{https://gjy3035.github.io/NWPU-Crowd-Sample-Code/}.","url_abs":"https://arxiv.org/abs/2001.03360v4","url_pdf":"https://arxiv.org/pdf/2001.03360v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nwpu-crowd-a-large-scale-benchmark-for-crowd","repo_url":"https://github.com/gjy3035/Awesome-Crowd-Counting","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"nwpu-crowd-a-large-scale-benchmark-for-crowd","repo_url":"https://github.com/gjy3035/NWPU-Crowd-Sample-Code","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"nwpu-crowd-a-large-scale-benchmark-for-crowd","repo_url":"https://github.com/MagazzuGaetano/Crowd-Counting-DEMO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"nwpu-crowd-a-large-scale-benchmark-for-crowd","repo_url":"https://github.com/gjy3035/NWPU-Crowd-Sample-Code-for-Localization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[{"slug":"nwpu-crowd","name":"NWPU-Crowd","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2001.03360","atlas_url":"https://app.syntology.ai/?focus=2001.03360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.03360"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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