Effect of Input Data Video Interval and Input Data Image Similarity on Learning Accuracy in 3D-CNN
Heeil Kim, Yeong-Jee Chung · International Journal of Internet, Broadcasting and Communication · 2021
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.