Ghost Graph Convolutional Network for Skeleton-based Action Recognition

Sungjun Jang, Heansung Lee, Suhwan Cho, Sungmin Woo, Sangyoun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Skeleton-based action recognition has attracted great attention in human action recognition. Existing methods for skeleton-based action recognition improve performance by designing deeper networks without considering the efficiency of the model. In this paper, we propose a simple and effective light weight graph convolutional network for skeleton-based action recognition. Our model is composed of a lightweight temporal convolutional network and spatial graph convolutional network using depthwise convolution. In addition, we propose a novel graph convolution that can take the multi-scale relationship of joints with low computational complexities. On the NTU RGB+D dataset, our proposed model achieves comparable or higher performance with much fewer parameters compared with baseline method.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665408578
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021 - Gangwon, Korea, Republic of
Duration: 2021 Nov 12021 Nov 3

Publication series

Name2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021

Conference

Conference2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021
Country/TerritoryKorea, Republic of
CityGangwon
Period21/11/121/11/3

Bibliographical note

Funding Information:
This work was supported by Hanwha Techwin.

Publisher Copyright:
© 2021 IEEE.

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering

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