Real time human motion recognition via spiking neural network

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.