{"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/exploring-structure-for-long-term-tracking-of","title":"Exploring Structure for Long-Term Tracking of Multiple Objects in Sports Videos","arxiv_id":"1612.06454","date":"2016-12-19","proceeding":null,"authors":["Henrique Morimitsu","Isabelle Bloch","Roberto M. Cesar-Jr"],"abstract":"In this paper, we propose a novel approach for exploiting structural\nrelations to track multiple objects that may undergo long-term occlusion and\nabrupt motion. We use a model-free approach that relies only on annotations\ngiven in the first frame of the video to track all the objects online, i.e.\nwithout knowledge from future frames. We initialize a probabilistic Attributed\nRelational Graph (ARG) from the first frame, which is incrementally updated\nalong the video. Instead of using the structural information only to evaluate\nthe scene, the proposed approach considers it to generate new tracking\nhypotheses. In this way, our method is capable of generating relevant object\ncandidates that are used to improve or recover the track of lost objects. The\nproposed method is evaluated on several videos of table tennis, volleyball, and\non the ACASVA dataset. The results show that our approach is very robust,\nflexible and able to outperform other state-of-the-art methods in sports videos\nthat present structural patterns.","url_abs":"http://arxiv.org/abs/1612.06454v1","url_pdf":"http://arxiv.org/pdf/1612.06454v1.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":"exploring-structure-for-long-term-tracking-of","repo_url":"https://github.com/henriquem87/structured-graph-tracker","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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}