Acquisition of Optimal Connection Patterns for Skeleton-based Action Recognition with Graph Convolutional Networks

Katsutoshi Shiraki; Tsubasa Hirakawa; Takayoshi Yamashita; Hironobu Fujiyoshi

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Paper

Acquisition of Optimal Connection Patterns for Skeleton-based Action Recognition with Graph Convolutional Networks

Topics: Categorization and Scene Understanding; Deep Learning for Visual Understanding ; Event and Human Activity Recognition; Video Surveillance and Event Detection

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, 302-309, 2020 , Valletta, Malta

Authors: Katsutoshi Shiraki ; Tsubasa Hirakawa ; Takayoshi Yamashita and Hironobu Fujiyoshi

Affiliation: Chubu University, Kasugai, Aichi, Japan

Keyword(s): Graph Convolutional Networks, Multitask Learning, Skeleton-based Action Recognition.

Abstract: Action recognition from skeletons is gaining attention since skeleton data can be easily obtained from depth sensors and highly accurate pose estimation methods such as OpenPose. A method using graph convolutional networks (GCN) has been proposed for action recognition using skeletons as input. Among the action recognition methods using GCN, spatial temporal GCN (ST-GCN) achieves a higher accuracy by capturing skeletal data as spatial and temporal graphs. However, because ST-GCN defines human skeleton patterns in advance and applies convolution processing, it is not possible to capture features that take into account the joint relationships specific to each action. The purpose of this work is to recognize actions considering the connection patterns specific to action classes. The optimal connection pattern is obtained by acquiring features of each action class by introducing multitask learning and selecting edges on the basis of the value of the weight matrix indicating the importanc e of the edges. Experimental results show that the proposed method has a higher classification accuracy than the conventional method. Moreover, we visualize the obtained connection patterns by the proposed method and show that our method can obtain specific connection patterns for each action class. (More)

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Paper citation in several formats:

Shiraki, K.; Hirakawa, T.; Yamashita, T. and Fujiyoshi, H. (2020). Acquisition of Optimal Connection Patterns for Skeleton-based Action Recognition with Graph Convolutional Networks. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 302-309. DOI: 10.5220/0008934603020309

@conference{visapp20,
author={Katsutoshi Shiraki. and Tsubasa Hirakawa. and Takayoshi Yamashita. and Hironobu Fujiyoshi.},
title={Acquisition of Optimal Connection Patterns for Skeleton-based Action Recognition with Graph Convolutional Networks},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={302-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008934603020309},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Acquisition of Optimal Connection Patterns for Skeleton-based Action Recognition with Graph Convolutional Networks
SN - 978-989-758-402-2
IS - 2184-4321
AU - Shiraki, K.
AU - Hirakawa, T.
AU - Yamashita, T.
AU - Fujiyoshi, H.
PY - 2020
SP - 302
EP - 309
DO - 10.5220/0008934603020309
PB - SciTePress