A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes
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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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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
- 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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