The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Method for detection of unsafe actions in power field based on edge computing architecture
Open accessPublished version of a preprint
Abstract
Due to the high risk factors in the electric power industry, the safety of power system can be improved by using the surveillance system to predict and warn the operators’ nonstandard and unsafe actions in real time. In this paper, aiming at the real-time and accuracy requirements in video intelligent surveillance, a method based on edge computing architecture is proposed to judge unsafe actions of electric power operations in time. In this method, the service of unsafe actions judgment is deployed to the edge cloud, which improves the real-time performance. In order to identify the action being executed, the end-to-end action recognition model proposed in this paper uses the Temporal Convolutional Neural Network (TCN) to extract local temporal features and a Gate Recurrent Unit (GRU) layer to extract global temporal features, which increases the accuracy of action fragment recognition. The result of action recognition is combined with the result of equipment target recognition based on the yolov3 model, and the classification rule is used to determine whether the current action is safe. Experiments show that the proposed method has better real-time performance, and the proposed action cognition is verified on the MSRAction Dataset, which improves the recognition accuracy of action segments. At the same time, the judgment results of unsafe actions also prove the effectiveness of the proposed method.
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Cites
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Research works printed but not linked: 10
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Metadata
- Authors
- Yin, Y., Lin, J., Sun, N., Zhu, Q., Zhang, S., Zhang, Y., & Liu, M.
- Affiliations
- Shandong University of Science and Technology (CN)
- Shandong University of Finance and Economics (CN)
- Shandong Electric Power College (CN)
- Corresponding author
- Liu, Ming
- Venue
- Journal of Cloud Computing
- Published
- 2021-02-22
- Volume
- 10
- Issue
- 1
- ISSN
- 2192-113X (ISSN-L)
- OpenAlex
- W3131256294
- Cited by
- 14
- References
- 34
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 1 · 2022: 3 · 2021: 1
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
- Locations
- Journal of Cloud Computing Advances Systems and Applications (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Young Scientists Fund (61703243)
- Natural Science Foundation of Shandong Province (ZR2018ZC0437)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- edge computing, video surveillance, human action recognition, temporal convolutional network, gated recurrent unit, YOLOv3, power field
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- Yin, Y., Lin, J., Sun, N., Zhu, Q., Zhang, S., Zhang, Y., & Liu, M. (2021). Method for detection of unsafe actions in power field based on edge computing architecture. Journal of Cloud Computing. https://doi.org/10.1186/s13677-021-00234-w
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