Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data

Author:

Pulgarin-Giraldo J. D.,Ruales-Torres A. A.,Alvarez-Meza A. M.,Castellanos-Dominguez G.

Publisher

Springer International Publishing

Reference18 articles.

1. Kale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. IJACI 7(2), 75–92 (2016)

2. Mandery, C., Plappert, M., Sol, J.B., Asfour, T.: Dimensionality reduction for whole-body human motion recognition. In: 19th International Conference on Information Fusion, FUSION, Heidelberg, Germany, 5–8 July 2016, pp. 355–362 (2016)

3. Althloothi, S., Mahoor, M.H., Zhang, X., Voyles, R.M.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recogn. 47(5), 1800–1812 (2014)

4. Li, M., Leung, H., Liu, Z., Zhou, L.: 3D human motion retrieval using graph kernels based on adaptive graph construction. Comput. Graph. Pergamon 54, 104–112 (2016)

5. Wang, L., Zhao, G., Cheng, L., Pietikainen, M.: Machine Learning for Vision-Based Motion Analysis, 1st edn. Springer, Heidelberg (2011)

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1. Smart Deep Learning Based Human Behaviour Classification for Video Surveillance;Computers, Materials & Continua;2022

2. A new hybrid deep learning model for human action recognition;Journal of King Saud University - Computer and Information Sciences;2020-05

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