{"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/towards-a-principled-integration-of-multi","title":"Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters","arxiv_id":"1705.04608","date":"2017-05-12","proceeding":null,"authors":["Lucas Beyer","Stefan Breuers","Vitaly Kurin","Bastian Leibe"],"abstract":"With the rise of end-to-end learning through deep learning, person detectors\nand re-identification (ReID) models have recently become very strong.\nMulti-camera multi-target (MCMT) tracking has not fully gone through this\ntransformation yet. We intend to take another step in this direction by\npresenting a theoretically principled way of integrating ReID with tracking\nformulated as an optimal Bayes filter. This conveniently side-steps the need\nfor data-association and opens up a direct path from full images to the core of\nthe tracker. While the results are still sub-par, we believe that this new,\ntight integration opens many interesting research opportunities and leads the\nway towards full end-to-end tracking from raw pixels.","url_abs":"http://arxiv.org/abs/1705.04608v2","url_pdf":"http://arxiv.org/pdf/1705.04608v2.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":"towards-a-principled-integration-of-multi","repo_url":"https://github.com/VisualComputingInstitute/towards-reid-tracking","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"towards-a-principled-integration-of-multi","repo_url":"https://github.com/VisualComputingInstitute/triplet-reid","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}