Action Recognition of Human’s Lower Limbs Based on a Human Joint
Abstract: To recognize the
actions of human’s lower limbs accurately and quickly, 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 Butterworth filter 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
characteristics with the change direction ofy coordinates. The proposed action
recognition method needs less action information and has a fast calculation
speed. Experiments proved the method had a high recognition rate and a good
application prospect.
Keywords: human action
characteristics, characteristic classification, improved self-organizing competitive
neural network; action recognition
Author: Feng Liang, Zhili
Zhang, Xiangyang Li, Yong Long, Zhao Tong
Journal Code: jptkomputergg160294
