Temporal recurrent networks for online action detection

Mingze Xu, Mingfei Gao, Yi-Ting Chen, Larry Davis, David Crandall

研究成果: Conference contribution同行評審

106 引文 斯高帕斯(Scopus)

摘要

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed. However, important real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, the Temporal Recurrent Network (TRN), to model greater temporal context of each frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14. The results show that TRN significantly outperforms the state-of-the-art.

原文English
主出版物標題Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面5531-5540
頁數10
ISBN(電子)9781728148038
DOIs
出版狀態Published - 27 10月 2019
事件17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
持續時間: 27 10月 20192 11月 2019

出版系列

名字Proceedings of the IEEE International Conference on Computer Vision
2019-October
ISSN(列印)1550-5499

Conference

Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
國家/地區Korea, Republic of
城市Seoul
期間27/10/192/11/19

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