Pitching Classification and Habit Detection by V-Net

Sota Kato, Kazuhiro Hotta

2020

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.

Paper Citation

in Harvard Style

Kato S. and Hotta K. (2020). Pitching Classification and Habit Detection by V-Net. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 615-621. DOI: 10.5220/0009347106150621

in Bibtex Style

@conference{visapp20,
author={Sota Kato and Kazuhiro Hotta},
title={Pitching Classification and Habit Detection by V-Net},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={615-621},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009347106150621},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Pitching Classification and Habit Detection by V-Net
SN - 978-989-758-402-2
AU - Kato S.
AU - Hotta K.
PY - 2020
SP - 615
EP - 621
DO - 10.5220/0009347106150621
PB - SciTePress