Multi-view Multi-modal Approach Based on 5S-CNN and BiLSTM Using Skeleton, Depth and RGB Data for Human Activity Recognition
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10324-4.pdf
Reference25 articles.
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3. Wang, P., Li, W., Gao, Z., Zhang, J., Tang, C., & Ogunbona, P. O. (2016). Action recognition from depth maps using deep convolutional neural networks. IEEE Transactions on Human-Machine Systems, 46(4), 498–509.
4. Wang, P., Li, W., Ogunbona, P., Gao, Z., & Zhang, H. (2014). Mining mid-level features for action recognition based on effective skeleton representation. In Digital lmage computing: techniques and applications (DlCTA), 2014 international conference on, IEEE, pp. 1–8.
5. Vemulapalli, R., Arrate, F., & Chellappa, R. (2014). Human action recognition by representing 3d skeletons as points in a lie group. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 588–595.
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