Real time human motion recognition via spiking neural network
Jing Yang, Qingyuan Wu, Maiqi Huang, Ting Luo · IOP Conference Series Materials Science and Engineering · 2018
Real time human action recognition is to recognize the human motion type based on skeleton movement in real time and is always a challenging task. In this paper, a novel method is proposed to accomplish the classification by using Spiking neural network (SNN) which is biology oriented neural network dealing with precise timing spikes. First, a new temporal encoding scheme is used to encode the real time motion capture data into a series of spikes and the according type of the motion is represented by a spike time. Second, a two-layered spiking neural network is initiated and trained through a gradient descent learning algorithm. The experimental results show that this method achieves a good learning precision and generalization.