Remarkable Skeleton Based Human Action Recognition

Authors

  • Sushma Jaiswal Guru Ghasidas Central University, Bilaspur (C.G.), India
  • Tarun Jaiswal Department of Computer Applications, NIT Raipur, Raipur, India

DOI:

https://doi.org/10.37256/aie.122020562

Keywords:

skeleton based human action recognition, CNN, SVM-GNN, machine learning, MS-G3D, DGNN

Abstract

Skeleton-based human-action-recognition (SBHAR) has wide applications in cognitive science and automatic surveillance. However, the most challenging and crucial task of the skeleton-based human-action-recognition (SBHAR) is a significant view variation while capturing the data. In this area, a significant amount of satisfactory work has already been done, which include the Red Green Blue (RGB) data method. The performance of the SBHAR is also affected by the various factors such as video frame setting, view variations in motion, different backgrounds and inter-personal differences. In this survey, we explicitly address these challenges and provide a complete overview of advancement in this field. The deep learning method has been used in this field for a long time, but so far, no research has fully demonstrated its usefulness. In this paper, we first highlight the need for action recognition and significance of 3D skeleton data and finally, we survey the largest 3D skeleton dataset, i.e. NTU-RGB+D and its new version NTU-RGB+D 120.

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Published

2020-09-29

How to Cite

1.
Sushma Jaiswal, Tarun Jaiswal. Remarkable Skeleton Based Human Action Recognition. Artificial Intelligence Evolution [Internet]. 2020 Sep. 29 [cited 2024 Jul. 13];1(2):108-21. Available from: https://ojs.wiserpub.com/index.php/AIE/article/view/562