지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.1
- 발행연도
- 2023.2
- 수록면
- 55 - 63 (9page)
- DOI
- 10.5573/IEIESPC.2023.12.1.55
이용수
초록· 키워드
오류제보하기
In recent years, due to the sudden outbreak of public health events, online teaching has become a mainstream teaching approach, and the number of teaching videos has increased rapidly. Therefore, extracting active image information from videos is of great importance in understanding video. This research proposes extracting image features from the spatiotemporal dimension based on deep learning, usinga spatiotemporal network for action recognition of skeletal action, and building a CSTGAT model based on a convolutional neural network. The experimental results show that the CSTGAT model has an accuracy of 98.47%, a precision rate of 97.43%, and a recall rate of 71.65% after being trained by the convolutional neural network. Furthermore, it only needs 217 iterations to achieve stable target convergence. After 100 tests, the F1 value of the CSTGAT model was 96.83%. In summary, the proposed model has high accuracy, a comprehensive query rate, and good model expressiveness. This model could provide a solution for intelligent longdistance interaction between a human and a machine and could be used in online teaching.
목차
Abstract
1. Introduction
2. Related Work
3. Construction of CSTGAT Model based on CNN
4. Performance Analysis of CSTGAT Model based on CNN
5. Conclusion
References
참고문헌 (21)
참고문헌 신청
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Tsai. MF, Chen. CH. “Spatial temporal variation graph convolutional networks (STV-GCN) for skeleton-based emotional action recognition,” IEEE Access, no. 9, pp. 13870-13877, 2021. Article (CrossRef Link)
Gao. P, Zhao. D, Chen. X. “Multi-dimensional data modelling of video image action recognition and motion capture in deep learning framework,” IET Image Processing, vol. 14, no. 7, pp. 1257-1264, 2020.Article (CrossRef Link)
Wu. H, Zhou. B, Zhu. K, Shang. C, Tam. HY, Lu. C. “Pattern recognition in distributed fiber-optic acoustic sensor using intensity and phase stacked convolutional neural network with data augmentation,” Optics Express, vol. 29, no. 3, pp. 3269-3283, 2021.A
Rastgoo. R, Kiani. K, Escalera. S. “Hand sign language recognition using multi-view hand skeleton,” Expert Systems with Applications, vol. 150, no. 8, pp. 113336, 2020. Article (CrossRef Link)
Tsai. MF, Chen. CH. “Spatial temporal variation graph convolutional networks (STV-GCN) for skeleton-based emotional action recognition,” IEEE Access, no. 9, pp. 13870-13877, 2021. Article (CrossRef Link)
Gao. P, Zhao. D, Chen. X. “Multi-dimensional data modelling of video image action recognition and motion capture in deep learning framework,” IET Image Processing, vol. 14, no. 7, pp. 1257-1264, 2020.Article (CrossRef Link)
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