Cited by 51Human Pose and Action RecognitionHand Gesture Recognition SystemsContext-Aware Activity Recognition Systems

A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes

Bilal, Muhammad, Maqsood, Muazzam, Yasmin, Sadaf, Hasan, Najam Ul, Rho, Seungmin·2022·The Journal of Supercomputing

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Crossref · 10.1007/s11227-021-03957-4

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43 of 43 references verified0 citation errors · 0 not checkable

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  • In [115], a spatiotemporal transfer-learning-based framework with an RNN and LSTM achieved 96.3% accuracy for recognizing similar or overlapping actions in sports activities.

    “The RNN with LSTM model achieved an accuracy of 96.3%.”

    From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [115]

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Authors
Bilal, M., Maqsood, M., Yasmin, S., Hasan, N. U., & Rho, S.
Affiliations
  • COMSATS University Islamabad (PK)
  • Dhofar University (OM)
  • Chung-Ang University (KR)
Corresponding author
  • Maqsood, Muazzam
  • Rho, Seungmin
Venue
The Journal of Supercomputing
Published
2021-07-13
Volume
78
Issue
2
Pages
2873-2908
ISSN
  • 0920-8542 (ISSN-L)
  • 1573-0484
OpenAlex
W3179851548
Cited by
51
References
43
Citations by year
2026: 9 · 2025: 10 · 2024: 9 · 2023: 12 · 2022: 8 · 2021: 3
FWCI
2.88
Type
journal article
Language
en
Open access
closed
Locations
The Journal of Supercomputing (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
  • Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
Keywords
human action recognition, spatiotemporal feature extraction, transfer learning, long video sequences, deep autoencoder, LSTM, fine-tuning

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