{"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/followme-efficient-online-min-cost-flow","title":"FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation","arxiv_id":"1407.6251","date":"2014-07-23","proceeding":"ICCV 2015 12","authors":["Philip Lenz","Andreas Geiger","Raquel Urtasun"],"abstract":"One of the most popular approaches to multi-target tracking is\ntracking-by-detection. Current min-cost flow algorithms which solve the data\nassociation problem optimally have three main drawbacks: they are\ncomputationally expensive, they assume that the whole video is given as a\nbatch, and they scale badly in memory and computation with the length of the\nvideo sequence. In this paper, we address each of these issues, resulting in a\ncomputationally and memory-bounded solution. First, we introduce a dynamic\nversion of the successive shortest-path algorithm which solves the data\nassociation problem optimally while reusing computation, resulting in\nsignificantly faster inference than standard solvers. Second, we address the\noptimal solution to the data association problem when dealing with an incoming\nstream of data (i.e., online setting). Finally, we present our main\ncontribution which is an approximate online solution with bounded memory and\ncomputation which is capable of handling videos of arbitrarily length while\nperforming tracking in real time. We demonstrate the effectiveness of our\nalgorithms on the KITTI and PETS2009 benchmarks and show state-of-the-art\nperformance, while being significantly faster than existing solvers.","url_abs":"http://arxiv.org/abs/1407.6251v2","url_pdf":"http://arxiv.org/pdf/1407.6251v2.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":[],"tasks":[{"task_slug":"multiple-object-tracking","task_name":"Multiple Object Tracking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multiple-object-tracking-on-kitti-test-online","task":"Multiple Object Tracking","dataset":"KITTI Test (Online Methods)","model":"mbodSSP","rank_in_archive_order":34,"of":34,"metrics":{"MOTA":"72.69"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1407.6251","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}