| Title |
Depth Image-Based Human Action Recognition Using Convolution Neural Network and Spatio-Temporal Templates |
| Authors |
음혁민(Eum, Hyukmin) ; 윤창용(Yoon, Changyong) |
| DOI |
https://doi.org/10.5370/KIEE.2016.65.10.1731 |
| Keywords |
Human action recognition ; Convolution neural network ; Spatio-temporal templates ; Motion history image ; Depth information |
| Abstract |
In this paper, a method is proposed to recognize human actions as nonverbal expression; the proposed method is composed of two steps which are action representation and action recognition. First, MHI(Motion History Image) is used in the action representation step. This method includes segmentation based on depth information and generates spatio-temporal templates to describe actions. Second, CNN(Convolution Neural Network) which includes feature extraction and classification is employed in the action recognition step. It extracts convolution feature vectors and then uses a classifier to recognize actions. The recognition performance of the proposed method is demonstrated by comparing other action recognition methods in experimental results. |