Skeleton-Based Student Activities Recognition from Classroom Videos
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50818-9_32
Reference8 articles.
1. Nguyen, P.-D., Nguyen, H.-Q., Nguyen, T.-B., Le, T.-L., Tran, T.-H., Vu, H., Huu, Q.N.: A new dataset and systematic evaluation of deep learning models for student activity recognition from classroom videos. In: 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 1–6 (2022)
2. Le, T.-H., Tran, H.-N., Nguyen, P.-D., Nguyen, H.-Q., Nguyen, T.-B., Tran, T.-H., Vu, H., Tran, T.-T., Le, T.-L.: Spatial and temporal hand-raising recognition from classroom videos using locality, relative position-aware non-local networks and hand tracking. Vietnam. J. Comput. Sci. 10(2), 243–271 (2023)
3. Zheng, R., Jiang, F., Shen, R.: Intelligent student behavior analysis system for real classrooms. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 9244–9248. IEEE (2020)
4. Li, W., Jiang, F., Shen, R.: Sleep gesture detection in classroom monitor system. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7640–7644. IEEE (2019)
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