{"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/181205131","title":"Poisson multi-Bernoulli mixture trackers: continuity through random finite sets of trajectories","arxiv_id":"1812.05131","date":"2018-12-12","proceeding":null,"authors":["Karl Granström","Lennart Svensson","Yuxuan Xia","Jason Williams","Angel F Garcia-Fernandez"],"abstract":"The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target\ndistribution for which the prediction and update are closed. It has a Poisson\nbirth process, and new Bernoulli components are generated on each new\nmeasurement as a part of the Bayesian measurement update. The PMBM filter is\nsimilar to the multiple hypothesis tracker (MHT), but seemingly does not\nprovide explicit continuity between time steps. This paper considers a recently\ndeveloped formulation of the multi-target tracking problem as a random finite\nset (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM\ntrackers. The PMBM trackers efficiently estimate the set of trajectories, and\nshare hypothesis structure with the PMBM filter. By showing that the prediction\nand update in the PMBM filter can be viewed as an efficient method for\ncalculating the time marginals of the RFS of trajectories, continuity in the\nsame sense as MHT is established for the PMBM filter.","url_abs":"http://arxiv.org/abs/1812.05131v1","url_pdf":"http://arxiv.org/pdf/1812.05131v1.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":"181205131","repo_url":"https://github.com/yuhsuansia/Extended-Target-PMBM-Tracker","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"181205131","repo_url":"https://github.com/yuhsuansia/Multi-scan-trajectory-PMBM-filter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"181205131","repo_url":"https://github.com/yuhsuansia/batch-tpmbm-using-mcmc-sampling","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"181205131","repo_url":"https://github.com/Agarciafernandez/MTT","is_official":0,"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}