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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.6
- 수록면
- 807 - 818 (12page)
이용수
초록· 키워드
오류제보하기
In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.
목차
ABSTRACT
1. INTRODUCTION
2. YOLO-LSTM-CONVOLUTIONAL NEURAL NETWORK
3. EXPERIMENTAL RESULTS AND ANALYSIS
4. CONCLUSION
REFERENCE
참고문헌 (17)
참고문헌 신청
G. Stavropoulos, D. Giakoumis, K. Moustakas, and D. Tzovaras, “Automatic Action Recognition for Assistive Robots to Support MCI Patients at Home,” Proceedings of the 10th International Conference on Pervasive Technologies Related to Assistive Environ- ments, pp. 366-371, 2017.
J.Y.H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond Short Snippets: Deep Networks for Video Classification,” Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. pp. 4694-4702, 2015.
A. Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S.W. Baik, “Action Recognition in Video Sequences Using Deep Bi-directional LSTM with CNN Features,” IEEE Access, Vol. 6, pp. 1155-1166, 2017.
J. Donahue, L.A. Hendricks, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-Term Recurrent Convolutional Networks for Visual Recognition and Description,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 4, pp. 677-691, 2016.
G. Yu and T. Li, “Recognition of Human Continuous Action with 3D CNN,” Proceedings of the 11th International Conference on Computer Vision Systems, pp. 314-322, 2017.
J.Y.H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, “Beyond Short Snippets: Deep Networks for Video Classification,” Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. pp. 4694-4702, 2015.
A. Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S.W. Baik, “Action Recognition in Video Sequences Using Deep Bi-directional LSTM with CNN Features,” IEEE Access, Vol. 6, pp. 1155-1166, 2017.
J. Donahue, L.A. Hendricks, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-Term Recurrent Convolutional Networks for Visual Recognition and Description,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 4, pp. 677-691, 2016.
G. Yu and T. Li, “Recognition of Human Continuous Action with 3D CNN,” Proceedings of the 11th International Conference on Computer Vision Systems, pp. 314-322, 2017.
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