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Real time human motion recognition via spiking neural network
| Content Provider | Scilit |
|---|---|
| Author | Yang, Jing Wu, Qingyuan Huang, Maiqi Luo, Ting |
| Copyright Year | 2018 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering 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. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/366/1/012042/pdf http://iopscience.iop.org/article/10.1088/1757-899X/366/1/012042/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/366/1/012042 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 1 |
| Volume Number | 366 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-06-13 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Information Systems Spiking Neural Network Real Time Spike Time Time Human |
| Content Type | Text |
| Resource Type | Article |
