{"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/resnetcrowd-a-residual-deep-learning","title":"ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification","arxiv_id":"1705.10698","date":"2017-05-30","proceeding":null,"authors":["Mark Marsden","Kevin McGuinness","Suzanne Little","Noel E. O'Connor"],"abstract":"In this paper we propose ResnetCrowd, a deep residual architecture for\nsimultaneous crowd counting, violent behaviour detection and crowd density\nlevel classification. To train and evaluate the proposed multi-objective\ntechnique, a new 100 image dataset referred to as Multi Task Crowd is\nconstructed. This new dataset is the first computer vision dataset fully\nannotated for crowd counting, violent behaviour detection and density level\nclassification. Our experiments show that a multi-task approach boosts\nindividual task performance for all tasks and most notably for violent\nbehaviour detection which receives a 9\\% boost in ROC curve AUC (Area under the\ncurve). The trained ResnetCrowd model is also evaluated on several additional\nbenchmarks highlighting the superior generalisation of crowd analysis models\ntrained for multiple objectives.","url_abs":"http://arxiv.org/abs/1705.10698v1","url_pdf":"http://arxiv.org/pdf/1705.10698v1.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":"resnetcrowd-a-residual-deep-learning","repo_url":"https://github.com/lcylmhlcy/ResnetCrowd-Caffe","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[{"slug":"multi-task-crowd","name":"Multi Task Crowd","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.10698","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}