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http://dx.doi.org/10.9717/kmms.2017.20.5.835

Study on Real-time Gesture Recognition based on Convolutional Neural Network for Game Applications  

Chae, Ji Hun (Dept. of Computer Engineering, Graduate School, Keimyung University)
Lim, Jong Heon (Dept. of Computer Engineering, Graduate School, Keimyung University)
Kim, Hae Sung (Faculty of Computer Engineering, Keimyung University)
Lee, Joon Jae (Faculty of Computer Engineering, Keimyung University)
Publication Information
Journal of Korea Multimedia Society / v.20, no.5, 2017 , pp. 835-843 More about this Journal
Abstract
Humans have often been used gesture to communicate with each other. The communication between computer and person was also not different. To interact with a computer, we command with gesture, keyboard, mouse and extra devices. Especially, the gesture is very useful in many environments such as gaming and VR(Virtual Reality), which requires high specification and rendering time. In this paper, we propose a gesture recognition method based on CNN model to apply to gaming and real-time applications. Deep learning for gesture recognition is processed in a separated server and the preprocessing for data acquisition is done a client PC. The experimental results show that the proposed method is in accuracy higher than the conventional method in game environment.
Keywords
Convolutional Neural Network; Gesture Recognition; Game Applications;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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