Fusing Local and Global Features for Human Action Recognition

Authors

Tang Chao, 1. Department of Computer Science 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 360054, China;;
Cao Feng, 4. School of Computer and Information Science, Shanxi University, Taiyuan 030006, China;;
Xiaofeng Wang, 1. Department of Computer Science and Technology, Hefei University, Hefei 230601, China;;
Xiaohong Tong, 5. Information Center, Hefei Technology College, Hefei 238000, China;

Abstract

Abstract: Recognizing human actions according to video features is an important research topic in a wide scope of applications. In this paper, we propose a robust human motion detection method that combines canny operator with the combination of local and global optic flow methods. Meanwhile, this paper presents a simple but efficient action recognition algorithm using fusion visual features. The mixed features fuse two action descriptors, namely centre distance-based space time interest point and curvature function-based Fourier descriptors. The frame-based human action classifier is developed using random forests algorithm. Experimental results show that the proposed method is accurate, efficient and robust compared with other supervised action recognition algorithms.

Recommended Citation

Chao, Tang; Zhang, Miaohui; Wei, Li; Feng, Cao; Wang, Xiaofeng; and Tong, Xiaohong (2019) "Fusing Local and Global Features for Human Action Recognition," Journal of System Simulation: Vol. 30: Iss. 7, Article 9.
DOI: 10.16182/j.issn1004731x.joss.201807009
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss7/9

First Page

2497

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201807009

Last Page

2506

CLC

TP391

Recommended Citation

Tang Chao, Zhang Miaohui, Li Wei, Cao Feng, Wang Xiaofeng, Tong Xiaohong. Fusing Local and Global Features for Human Action Recognition[J]. Journal of System Simulation, 2018, 30(7): 2497-2506.

Corresponding Author

Miaohui Zhang,

DOI

10.16182/j.issn1004731x.joss.201807009