{"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/temporal-recurrent-networks-for-online-action","title":"Temporal Recurrent Networks for Online Action Detection","arxiv_id":"1811.07391","date":"2018-11-18","proceeding":"ICCV 2019 10","authors":["Mingze Xu","Mingfei Gao","Yi-Ting Chen","Larry S. Davis","David J. Crandall"],"abstract":"Most work on temporal action detection is formulated as an offline problem,\nin which the start and end times of actions are determined after the entire\nvideo is fully observed. However, important real-time applications including\nsurveillance and driver assistance systems require identifying actions as soon\nas each video frame arrives, based only on current and historical observations.\nIn this paper, we propose a novel framework, Temporal Recurrent Network (TRN),\nto model greater temporal context of a video frame by simultaneously performing\nonline action detection and anticipation of the immediate future. At each\nmoment in time, our approach makes use of both accumulated historical evidence\nand predicted future information to better recognize the action that is\ncurrently occurring, and integrates both of these into a unified end-to-end\narchitecture. We evaluate our approach on two popular online action detection\ndatasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14.\nThe results show that TRN significantly outperforms the state-of-the-art.","url_abs":"http://arxiv.org/abs/1811.07391v2","url_pdf":"http://arxiv.org/pdf/1811.07391v2.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":"temporal-recurrent-networks-for-online-action","repo_url":"https://github.com/xumingze0308/TRN.pytorch","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"action-detection","task_name":"Action Detection"},{"task_slug":"online-action-detection","task_name":"Online Action Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/online-action-detection-on-thumos-14","task":"Online Action Detection","dataset":"THUMOS'14","model":"TRN","rank_in_archive_order":13,"of":15,"metrics":{"mAP":"62.1"},"uses_additional_data":false},{"leaderboard":"/sota/online-action-detection-on-tvseries","task":"Online Action Detection","dataset":"TVSeries","model":"TRN","rank_in_archive_order":12,"of":13,"metrics":{"mCAP":"86.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.07391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.07391"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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