Action Recognition of Human’s Lower Limbs Based on a Human Joint

Feng Liang, Zhili Zhang, Xiangyang Li, Yong Long, Zhao Tong

Abstract


In order to recognize the actions of human’s lower limbs, a novel action recognition method based on a human joint was proposed. Firstly, hip joint was chosen as the recognition object, its y coordinates were as recognition parameter, and human action characteristics were achieved based on filtering and wavelet transform. Secondly, an improved self-organizing competitive neural network was proposed, which could classify the action characteristics automatically according to the classification number. The classification results of motion capture data proved the validity of the neural network. Finally,an action recognition method based on hidden Markov model (HMM) was introduced to realize the recognition of classification results of human action characteristicswith the change direction of y coordinates. The proposed action recognition method needs less action information and has a fast calculation speed. Experiments proved the method hada high recognition rate and a good application prospect.

Keywords


human action characteristics, characteristic classification, improved self-organizing competitive neural network, action recognition

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v14i3.3556

Article Metrics

Abstract view : 228 times
PDF - 224 times

Refbacks



Copyright (c) 2019 Universitas Ahmad Dahlan

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

View TELKOMNIKA Stats