{"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-probabilistic-logic-programming-event","title":"A Probabilistic Logic Programming Event Calculus","arxiv_id":"1204.1851","date":"2012-04-09","proceeding":null,"authors":["Anastasios Skarlatidis","Alexander Artikis","Jason Filippou","Georgios Paliouras"],"abstract":"We present a system for recognising human activity given a symbolic\nrepresentation of video content. The input of our system is a set of\ntime-stamped short-term activities (STA) detected on video frames. The output\nis a set of recognised long-term activities (LTA), which are pre-defined\ntemporal combinations of STA. The constraints on the STA that, if satisfied,\nlead to the recognition of a LTA, have been expressed using a dialect of the\nEvent Calculus. In order to handle the uncertainty that naturally occurs in\nhuman activity recognition, we adapted this dialect to a state-of-the-art\nprobabilistic logic programming framework. We present a detailed evaluation and\ncomparison of the crisp and probabilistic approaches through experimentation on\na benchmark dataset of human surveillance videos.","url_abs":"http://arxiv.org/abs/1204.1851v2","url_pdf":"http://arxiv.org/pdf/1204.1851v2.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-probabilistic-logic-programming-event","repo_url":"https://github.com/MarcRoigVilamala/DeepProbCEP","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-probabilistic-logic-programming-event","repo_url":"https://github.com/dais-ita/deepprobcep","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}