RGB+2D skeleton: local hand-crafted and 3D convolution feature coding for action recognition
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-021-01868-8.pdf
Reference27 articles.
1. Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. Pattern Recognit. Lett. 119, 3–11 (2019)
2. Herath, S., Harandi, M., Porikli, F.: Going deeper into action recognition: a survey. Image Visiom Comput. 60, 4–21 (2017)
3. Zhang, H.B., Zhang, Y.X., Zhong, B., Lei, Q., Yang, L., Du, J.X., Chen, D.S.: A comprehensive survey of vision-based human action recognition methods. Sensors 19(5), (2019)
4. DasDawn, D., Shaikh, S.: A comprehensive survey of human action recognition with spatio-temporal interest point (stip) detector. Visual Comput., 32(3) (2016)
5. Presti, L.L., Cascia, M.L.: 3D skeleton-based human action classification: a survey. Pattern Recognit. 53, 130–147 (2016)
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