Deep learning-based multi-modal approach using RGB and skeleton sequences for human activity recognition

Author:

Verma Pratishtha,Sah Animesh,Srivastava Rajeev

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Information Systems,Software

Reference36 articles.

1. Varol, G., Laptev, I., Schmid, C.: Long-term temporal convolutions for action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1510–1517 (2017)

2. Song, S., Lan, C., Xing, J., Zeng, W., Liu, J.: An end-to-end spatio-temporal attention model for human action recognition from skeleton data. In: Thirty-First AAAI Conference on Artificial Intelligence, pp. 4263–4270 (2017)

3. Yun, K., Honorio, J., Chattopadhyay, D., Berg, T.L., Samaras, D.: Two-person interaction detection using body-pose features and multiple instance learning. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 28–35. IEEE, New York (2012)

4. Sung, J., Ponce, C., Selman, B., Saxena, A.: Unstructured human activity detection from RGBD images. In: 2012 IEEE International Conference on Robotics and Automation, pp. 842–849. IEEE, New York (2012)

5. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 9–14. IEEE, New York (2010)

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