International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Effect of Input Data Video Interval and Input Data Image Similarity on Learning Accuracy in 3D-CNN

  • Kim, Heeil (Department of Computer Engineering, Wonkwang University) ;
  • Chung, Yeongjee (Department of Computer and Software Engineering, Wonkwang University)
  • Received : 2021.04.06
  • Accepted : 2021.04.15
  • Published : 2021.05.31
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Abstract

3D-CNN is one of the deep learning techniques for learning time series data. However, these three-dimensional 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 measuring and normalizing the similarity of images through interclass 2D cross correlation analysis. This experiment 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.

Keywords

Acknowledgement

This paper was supported by Wonkwang University, Iksan Korea in 2021.

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