{"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/decidenet-counting-varying-density-crowds","title":"DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation","arxiv_id":"1712.06679","date":"2017-12-18","proceeding":"CVPR 2018 6","authors":["Jiang Liu","Chenqiang Gao","Deyu Meng","Alexander G. Hauptmann"],"abstract":"In real-world crowd counting applications, the crowd densities vary greatly\nin spatial and temporal domains. A detection based counting method will\nestimate crowds accurately in low density scenes, while its reliability in\ncongested areas is downgraded. A regression based approach, on the other hand,\ncaptures the general density information in crowded regions. Without knowing\nthe location of each person, it tends to overestimate the count in low density\nareas. Thus, exclusively using either one of them is not sufficient to handle\nall kinds of scenes with varying densities. To address this issue, a novel\nend-to-end crowd counting framework, named DecideNet (DEteCtIon and Density\nEstimation Network) is proposed. It can adaptively decide the appropriate\ncounting mode for different locations on the image based on its real density\nconditions. DecideNet starts with estimating the crowd density by generating\ndetection and regression based density maps separately. To capture inevitable\nvariation in densities, it incorporates an attention module, meant to\nadaptively assess the reliability of the two types of estimations. The final\ncrowd counts are obtained with the guidance of the attention module to adopt\nsuitable estimations from the two kinds of density maps. Experimental results\nshow that our method achieves state-of-the-art performance on three challenging\ncrowd counting datasets.","url_abs":"http://arxiv.org/abs/1712.06679v2","url_pdf":"http://arxiv.org/pdf/1712.06679v2.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":"decidenet-counting-varying-density-crowds","repo_url":"https://github.com/Sarathismg/Categorized-Crowd-Count-Test-on-Decidenet-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"},{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/crowd-counting-on-worldexpo10","task":"Crowd Counting","dataset":"WorldExpo’10","model":"DecideNet","rank_in_archive_order":10,"of":15,"metrics":{"Average MAE":"9.23"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1712.06679","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}