Vision-Based Human Activity Recognition Using CNN and LSTM Architecture
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56700-1_10
Reference27 articles.
1. Abbaspour, S., Fotouhi, F., Sedaghatbaf, A., Fotouhi, H., Vahabi, M., Linden, M.: A comparative analysis of hybrid deep learning models for human activity recognition. Sensors 20, 5707 (2020)
2. Ke, S.R., Thuc, H.L.U., Lee, Y.J., Hwang, J.N., Yoo, J.H., Choi, K.H.: A review on video-based human activity recognition. Computers 2(2), 88–131 (2013)
3. Singh, J., Sinha, S.: Video Based Human Activity Recognition Surveillance System
4. Hussain, Z., Sheng, Q.Z., Zhang, W.E.: A review and categorization of techniques on device-free human activity recognition. J. Netw. Comput. Appl. 167, 102738 (2020)
5. Gowda, S.N., Rohrbach, M., Sevilla-Lara, L.: Smart frame selection for action recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 2, pp. 1451–1459 (2021)
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