{"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/single-image-crowd-counting-via-multi-column-1","title":"Single-Image Crowd Counting via Multi-Column Convolutional Neural Network","arxiv_id":null,"date":"2016-01-01","proceeding":"Conference 2016 1","authors":["Yingying Zhang","Desen Zhou","Siqin Chen","Shenghua Gao","Yi Ma"],"abstract":"This paper aims to develop a method than can accurately\r\nestimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end,\r\nwe have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the\r\nimage to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution.\r\nBy utilizing filters with receptive fields of different sizes, the\r\nfeatures learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image\r\nresolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do\r\nnot need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work,\r\nwe have collected and labelled a large new dataset that\r\nincludes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing\r\ndatasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method\r\noutperforms all existing methods. In addition, experiments\r\nshow that our model, once trained on one dataset, can be\r\nreadily transferred to a new dataset.","url_abs":"https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.pdf","url_pdf":"https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.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":"single-image-crowd-counting-via-multi-column-1","repo_url":"https://github.com/CommissarMa/MCNN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"single-image-crowd-counting-via-multi-column-1","repo_url":"https://github.com/mindspore-ai/models/tree/master/official/cv/MCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"single-image-crowd-counting-via-multi-column-1","repo_url":"https://github.com/svishwa/crowdcount-mcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"single-image-crowd-counting-via-multi-column-1","repo_url":"https://github.com/yangyucheng000/MCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"}],"methods":[],"datasets_introduced":[{"slug":"shanghaitech","name":"ShanghaiTech","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/crowd-counting-on-shanghaitech-a","task":"Crowd Counting","dataset":"ShanghaiTech A","model":"MCNN","rank_in_archive_order":34,"of":35,"metrics":{"MAE":"110.2"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-shanghaitech-b","task":"Crowd Counting","dataset":"ShanghaiTech B","model":"MCNN","rank_in_archive_order":31,"of":32,"metrics":{"MAE":"26.4"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-ucf-cc-50","task":"Crowd Counting","dataset":"UCF CC 50","model":"MCNN","rank_in_archive_order":20,"of":22,"metrics":{"MAE":"377.6"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-ucf-qnrf","task":"Crowd Counting","dataset":"UCF-QNRF","model":"MCNN","rank_in_archive_order":22,"of":23,"metrics":{"MAE":"277"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-venice","task":"Crowd Counting","dataset":"Venice","model":"MCNN","rank_in_archive_order":5,"of":5,"metrics":{"MAE":"145.4"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-worldexpo10","task":"Crowd Counting","dataset":"WorldExpo’10","model":"MCNN","rank_in_archive_order":14,"of":15,"metrics":{"Average MAE":"11.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}