The method of table tennis players' posture recognition based on a genetic algorithm
by Jiesen Yin
International Journal of Biometrics (IJBM), Vol. 13, No. 2/3, 2021

Abstract: In order to overcome the problems of poor denoising effect, low recognition efficiency and low recognition accuracy of the existing methods of table tennis players' posture recognition, this paper proposes a method of table tennis players' posture recognition based on a genetic algorithm. The optimisation model of multi-objective key frame extraction is constructed, and the optimisation model of multi-objective key frame extraction is solved by genetic algorithm to obtain the key frame. Kalman filter is used to remove the noise in the key frame, eliminate the interference of the noise on the recognition result, and shorten the recognition time. According to the results of noise removal, the dynamic time warping algorithm is used to recognise table tennis players' posture. The experimental results show that the proposed method has good denoising effect, high recognition efficiency and high recognition accuracy, with the highest recognition accuracy of 98.7%.

Online publication date: Thu, 29-Apr-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com