Human Action Recognition Method Based on Key Frames

Authors

Xiangbin Shi, 1. Department of Computer, Shenyang Aerospace University, Shenyang 110136, China;;2. Liaoning General Aviation Key Laboratory, Shenyang Aerospace University, Shenyang 110136, China;;3. College of Information, Liaoning University, Shenyang 110036, China;
Shuanpeng Liu, 1. Department of Computer, Shenyang Aerospace University, Shenyang 110136, China;;
Deyuan Zhang, 1. Department of Computer, Shenyang Aerospace University, Shenyang 110136, China;;

Abstract

Abstract: More and more researchers have begun to study the human action recognition based on depth information and skeleton information since the Kinect has been released. A method of human action recognition based on the skeleton feature of key frames is proposed in order to improve the accuracy and timeliness of the human action recognition, and reduce the computational complexity. The clustered data was obtained by using K-means clustering algorithm, and then the key frames were extracted by using the clustered data. Two features for human action recognition were extracted, one is the feature of the position of human joint, another is the feature of the skeleton angle between rigid body and rigid body. The sequence of action video was classified through the SVM classifier. This method leads to a more accurate recognition rate, and the real-time capability has been improved at the same time according to the result showed on the data set MSR-DailyActivity3D.

Recommended Citation

Shi, Xiangbin; Liu, Shuanpeng; and Zhang, Deyuan (2020) "Human Action Recognition Method Based on Key Frames," Journal of System Simulation: Vol. 27: Iss. 10, Article 26.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss10/26

First Page

2401

Revised Date

2015-07-30

DOI Link

https://doi.org/

Last Page

2408

CLC

TP391.4

Recommended Citation

Shi Xiangbin, Liu Shuanpeng, Zhang Deyuan. Human Action Recognition Method Based on Key Frames[J]. Journal of System Simulation, 2015, 27(10): 2401-2408.