Pitching Classification and Habit Detection by V-Net

Sota Kato, Kazuhiro Hotta

Abstract

In this paper, we propose a method that is classified pitching motions using deep learning and detected the habits of pitching. In image classification, there is a method called Grad-CAM to visualize the location related to classification. However, it is difficult to apply the Grad-CAM to conventional video classification methods using 3D-Convolution. To solve this problem, we propose a video classification method based on V-Net. By reconstructing input video, it is possible to visualize the frame and location related to classification result based on Grad-CAM. In addition, we improved the classification accuracy in comparison with conventional methods using 3D-Convolution and reconstruction. From experimental results, we confirmed the effectiveness of our method.

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