Human Action Recognition Based on Depth Image

Authors

Tang Chao, 1. Department of Computer Scince and Technology, Hefei University, Hefei 230601, China; ;
Miaohui Zhang, 2. Energy Research Institute, Jiangxi Academy of Sciences, Nanchang 330096, China; ;
Li Wei, 3. School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China; ;
Cao Feng, 4. School of Computer and Information Science, Shanxi University, Taiyuan 030006, China; ;
Xiaofeng Wang, 1. Department of Computer Scince and Technology, Hefei University, Hefei 230601, China; ;
Xiaohong Tong, 5. Information center, Hefei Technology College, Hefei 238000, China;

Abstract

Abstract: Because of the complexity and non-rigidity of human actions, traditional human action recognition based on RGB video data is a very challenging research topic. According to some deficiencies of existing recognition method based on RGB video data, a novel human action recognition method is proposed based on depth image data. In this new method, the block mean feature in the depth difference motion historical image is fused with the Gabor feature as mixed features and then a rotation forest algorithm is used to model. The experimental results show that the proposed method is simple, fast and efficient compared with other supervised action recognition algorithms on DHA depth datasets.

Recommended Citation

Chao, Tang; Zhang, Miaohui; Wei, Li; Feng, Cao; Wang, Xiaofeng; and Tong, Xiaohong (2019) "Human Action Recognition Based on Depth Image," Journal of System Simulation: Vol. 30: Iss. 5, Article 3.
DOI: 10.16182/j.issn1004731x.joss.201805003
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss5/3

First Page

1641

Revised Date

2017-09-18

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201805003

Last Page

1649

CLC

TP391

Recommended Citation

Tang Chao, Zhang Miaohui, Li Wei, Cao Feng, Wang Xiaofeng, Tong Xiaohong. Human Action Recognition Based on Depth Image[J]. Journal of System Simulation, 2018, 30(5): 1641-1649.

Corresponding Author

Miaohui Zhang,

DOI

10.16182/j.issn1004731x.joss.201805003