research-article

Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition

Authors:

Baocai YinAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 2

Article No.: 32, Pages 1 - 23

https://doi.org/10.1145/3434746

Published: 05 March 2021 Publication History

Abstract

Multi-view human action recognition remains a challenging problem due to large view changes. In this article, we propose a transfer learning-based framework called transferable dictionary learning and view adaptation (TDVA) model for multi-view human action recognition. In the transferable dictionary learning phase, TDVA learns a set of view-specific transferable dictionaries enabling the same actions from different views to share the same sparse representations, which can transfer features of actions from different views to an intermediate domain. In the view adaptation phase, TDVA comprehensively analyzes global, local, and individual characteristics of samples, and jointly learns balanced distribution adaptation, locality preservation, and discrimination preservation, aiming at transferring sparse features of actions of different views from the intermediate domain to a common domain. In other words, TDVA progressively bridges the distribution gap among actions from various views by these two phases. Experimental results on IXMAS, ACT4², and NUCLA action datasets demonstrate that TDVA outperforms state-of-the-art methods.

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Cited By

Ye M(2024)Application of 3D recognition algorithm based on spatio-temporal graph convolutional network in basketball pose estimationInternational Journal for Simulation and Multidisciplinary Design Optimization10.1051/smdo/202400415(9)Online publication date: 12-Apr-2024
https://doi.org/10.1051/smdo/2024004
Reeja SSoumya TDeepthi P(2024)A Study on Vision-Based Human Activity Recognition ApproachesModeling, Simulation and Optimization10.1007/978-981-99-6866-4_17(235-248)Online publication date: 20-Feb-2024
https://doi.org/10.1007/978-981-99-6866-4_17
Liu BSun PXiao YZhao SLi XPeng TZheng ZHuang Y(2023)Dictionary-Based Multi-View Learning With Privileged InformationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.324760034:5(3523-3537)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3247600
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Index Terms

Joint Transferable Dictionary Learning and View Adaptation for Multi-view Human Action Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 2

Survey Paper and Regular Papers

April 2021

524 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3446665

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 05 March 2021

Accepted: 01 November 2020

Revised: 01 September 2020

Received: 01 September 2019

Published in TKDD Volume 15, Issue 2

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Cited By

Ye M(2024)Application of 3D recognition algorithm based on spatio-temporal graph convolutional network in basketball pose estimationInternational Journal for Simulation and Multidisciplinary Design Optimization10.1051/smdo/202400415(9)Online publication date: 12-Apr-2024
https://doi.org/10.1051/smdo/2024004
Reeja SSoumya TDeepthi P(2024)A Study on Vision-Based Human Activity Recognition ApproachesModeling, Simulation and Optimization10.1007/978-981-99-6866-4_17(235-248)Online publication date: 20-Feb-2024
https://doi.org/10.1007/978-981-99-6866-4_17
Liu BSun PXiao YZhao SLi XPeng TZheng ZHuang Y(2023)Dictionary-Based Multi-View Learning With Privileged InformationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.324760034:5(3523-3537)Online publication date: 22-Feb-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3247600
Liu ZWu YYin ZGao C(2023)Unsupervised video segmentation for multi-view daily action recognitionImage and Vision Computing10.1016/j.imavis.2023.104687134(104687)Online publication date: Jun-2023
https://doi.org/10.1016/j.imavis.2023.104687
Momin MSufian ABarman DDutta PDong MLeo M(2022)In-Home Older Adults’ Activity Pattern Monitoring Using Depth Sensors: A ReviewSensors10.3390/s2223906722:23(9067)Online publication date: 23-Nov-2022
https://doi.org/10.3390/s22239067
Sun BKong DWang SLi JYin BLuo X(2022)GAN for vision, KG for relation: A two-stage network for zero-shot action recognitionPattern Recognition10.1016/j.patcog.2022.108563126(108563)Online publication date: Jun-2022
https://doi.org/10.1016/j.patcog.2022.108563
Liu ZWang LYin ZXue Y(2022)Task-driven joint dictionary learning model for multi-view human action recognitionDigital Signal Processing10.1016/j.dsp.2022.103487126(103487)Online publication date: Jun-2022
https://doi.org/10.1016/j.dsp.2022.103487
Nafea OAbdul WMuhammad G(2022)Multi-sensor human activity recognition using CNN and GRUInternational Journal of Multimedia Information Retrieval10.1007/s13735-022-00234-911:2(135-147)Online publication date: 19-Apr-2022
https://doi.org/10.1007/s13735-022-00234-9

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