research-article

Fast Action Localization in Large-Scale Video Archives

Authors:

Andrei Stoian,

Marin Ferecatu,

Jenny Benois-Pineau,

Michel CrucianuAuthors Info & Claims

IEEE Transactions on Circuits and Systems for Video Technology, Volume 26, Issue 10

Pages 1917 - 1930

https://doi.org/10.1109/TCSVT.2015.2475835

Published: 01 October 2016 Publication History

Abstract

Finding content in large video archives has so far required textual annotation to enable search by keywords. Our aim is to support retrieval from such archives using queries based on the example video clips that contain meaningful human actions. We propose a solution for the scalable search of actions in large-scale archives by leveraging the complementarity between the description at the frame level and the aggregation in time of descriptors. To permit fast search, we introduce a two-level cascade. The inexpensive first level employs aggregation to filter out a large part of the video. At the second level, aided by feature selection, a more discriminative comparison by frame alignment ranks the remaining video sequences. We improve upon the state of the art on popular data sets, and we introduce and show the results on a novel video archive data set that is significantly larger than previous ones.

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Moniruzzaman MYin Z(2023)Collaborative Foreground, Background, and Action Modeling Network for Weakly Supervised Temporal Action LocalizationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.327289133:11(6939-6951)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3272891
Moniruzzaman MYin ZHe ZLeu MQin R(2023)Jointly-Learnt Networks for Future Action Anticipation via Self-Knowledge Distillation and Cycle ConsistencyIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.323202133:7(3243-3256)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TCSVT.2022.3232021
Khan MZhang YKhan SAttique MRehman ASeo S(2021)A resource conscious human action recognition framework using 26-layered deep convolutional neural networkMultimedia Tools and Applications10.1007/s11042-020-09408-180:28-29(35827-35849)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11042-020-09408-1
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cover image IEEE Transactions on Circuits and Systems for Video Technology

IEEE Transactions on Circuits and Systems for Video Technology Volume 26, Issue 10

October 2016

187 pages

ISSN:1051-8215

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IEEE Press

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Published: 01 October 2016

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Moniruzzaman MYin Z(2023)Collaborative Foreground, Background, and Action Modeling Network for Weakly Supervised Temporal Action LocalizationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.327289133:11(6939-6951)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3272891
Moniruzzaman MYin ZHe ZLeu MQin R(2023)Jointly-Learnt Networks for Future Action Anticipation via Self-Knowledge Distillation and Cycle ConsistencyIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.323202133:7(3243-3256)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TCSVT.2022.3232021
Khan MZhang YKhan SAttique MRehman ASeo S(2021)A resource conscious human action recognition framework using 26-layered deep convolutional neural networkMultimedia Tools and Applications10.1007/s11042-020-09408-180:28-29(35827-35849)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11042-020-09408-1
Martin PBenois-Pineau JPéteri RMorlier J(2020)Fine grained sport action recognition with Twin spatio-temporal convolutional neural networksMultimedia Tools and Applications10.1007/s11042-020-08917-379:27-28(20429-20447)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s11042-020-08917-3
Kumar NSukavanam N(2020)Weakly supervised deep network for spatiotemporal localization and detection of human actions in wild conditionsThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-019-01777-536:9(1809-1821)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s00371-019-01777-5

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