{"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/a-hierarchical-deep-temporal-model-for-group","title":"A Hierarchical Deep Temporal Model for Group Activity Recognition","arxiv_id":"1511.06040","date":"2015-11-19","proceeding":"CVPR 2016 6","authors":["Moustafa Ibrahim","Srikanth Muralidharan","Zhiwei Deng","Arash Vahdat","Greg Mori"],"abstract":"In group activity recognition, the temporal dynamics of the whole activity\ncan be inferred based on the dynamics of the individual people representing the\nactivity. We build a deep model to capture these dynamics based on LSTM\n(long-short term memory) models. To make use of these ob- servations, we\npresent a 2-stage deep temporal model for the group activity recognition\nproblem. In our model, a LSTM model is designed to represent action dynamics of\nin- dividual people in a sequence and another LSTM model is designed to\naggregate human-level information for whole activity understanding. We evaluate\nour model over two datasets: the collective activity dataset and a new volley-\nball dataset. Experimental results demonstrate that our proposed model improves\ngroup activity recognition perfor- mance with compared to baseline methods.","url_abs":"http://arxiv.org/abs/1511.06040v2","url_pdf":"http://arxiv.org/pdf/1511.06040v2.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":"a-hierarchical-deep-temporal-model-for-group","repo_url":"https://github.com/mostafa-saad/deep-activity-rec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-2-Clause"}}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"group-activity-recognition","task_name":"Group Activity Recognition"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[{"slug":"volleyball","name":"Volleyball","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1511.06040","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}