{"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/joint-probabilistic-data-association","title":"Joint Probabilistic Data Association Revisited","arxiv_id":null,"date":"2015-12-01","proceeding":"ICCV 2015 12","authors":["Seyed Hamid Rezatofighi","Anton Milan","Zhen Zhang","Qinfeng Shi","Anthony Dick","Ian Reid"],"abstract":"In this paper, we revisit the joint probabilistic data association (JPDA) technique and propose a novel solution based on recent developments in finding the m-best solutions to an integer linear program. The key advantage of this approach is that it makes JPDA computationally tractable in applications with high target and/or clutter density, such as spot tracking in fluorescence microscopy sequences and pedestrian tracking in surveillance footage. We also show that our JPDA algorithm embedded in a simple tracking framework is surprisingly competitive with state-of-the-art global tracking methods in these two applications, while needing considerably less processing time.","url_abs":"http://openaccess.thecvf.com/content_iccv_2015/html/Rezatofighi_Joint_Probabilistic_Data_ICCV_2015_paper.html","url_pdf":"http://openaccess.thecvf.com/content_iccv_2015/papers/Rezatofighi_Joint_Probabilistic_Data_ICCV_2015_paper.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":"joint-probabilistic-data-association","repo_url":"https://github.com/apennisi/jpdaf_tracking","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}