Earticle

초록

영어

3D-CNN is one of the deep learning techniques for learning time series data. However, these three-dimen-sional learning can generate many parameters, requiring high performance or having a significant impact on learning speed. We will use these 3D-CNNs to learn hand gesture and find the parameters that showed the highest accuracy, and then analyze how the accuracy of 3D-CNN varies through input data changes without any structural changes in 3D-CNN. First, choose the interval of the input data. This adjusts the ratio of the stop interval to the gesture interval. Secondly, the corresponding interframe mean value is obtained by meas-uring and normalizing the similarity of images through interclass 2D cross correlation analysis. This experi-ment demonstrates that changes in input data affect learning accuracy without structural changes in 3D-CNN. In this paper, we proposed two methods for changing input data. Experimental results show that input data can affect the accuracy of the model.

목차

Abstract
1. Introduction
2. Related Work
3. Train Hand Gesture With 3D-CNN
3.1 3D-CNN Architecture
3.2 Data Set
3.3. Train Hand Gesture
4. Methods for selecting input data.
4.1. Through Frame Intervals
4.2. Through 2D Cross Correlation Between Input Data
5. Result
6. Conclusion
Acknowledgement
References

키워드

저자

• Heeil Kim [ Master Course, Department of Computer Engineering, Wonkwang University, Korea ]
• Yeongjee Chung [ Professor, Department of Computer and Software Engineering, Wonkwang University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380