{"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/adaptive-scenario-discovery-for-crowd","title":"Adaptive Scenario Discovery for Crowd Counting","arxiv_id":"1812.02393","date":"2018-12-06","proceeding":null,"authors":["Xingjiao Wu","Yingbin Zheng","Hao Ye","Wenxin Hu","Jing Yang","Liang He"],"abstract":"Crowd counting, i.e., estimation number of the pedestrian in crowd images, is\nemerging as an important research problem with the public security\napplications. A key component for the crowd counting systems is the\nconstruction of counting models which are robust to various scenarios under\nfacts such as camera perspective and physical barriers. In this paper, we\npresent an adaptive scenario discovery framework for crowd counting. The system\nis structured with two parallel pathways that are trained with different sizes\nof the receptive field to represent different scales and crowd densities. After\nensuring that these components are present in the proper geometric\nconfiguration, a third branch is designed to adaptively recalibrate the\npathway-wise responses by discovering and modeling the dynamic scenarios\nimplicitly. Our system is able to represent highly variable crowd images and\nachieves state-of-the-art results in two challenging benchmarks.","url_abs":"http://arxiv.org/abs/1812.02393v2","url_pdf":"http://arxiv.org/pdf/1812.02393v2.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":"adaptive-scenario-discovery-for-crowd","repo_url":"https://github.com/pxq0312/ASD-crowd-counting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}