Skeleton-based action recognition for industrial packing process

Zhenhui Chen, Haiyang Hu, Zhongjin Li, Xingchen Qi, Haiping Zhang, Hua Hu, Victor Chang

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

1 Citation (Scopus)

Abstract

The applications of action recognition in real-world scenarios are challenging. Although state-of-the-art methods have demonstrated good performance on large scale datasets, we still face complex practical problems and inappropriate models. In this work, we propose a novel local image directed graph neural network (LI-DGNN) to solve a real-world production scenario problem which is the completeness identification of accessories during the range hood packing process in a kitchen appliance manufacturing workshop. LI-DGNN integrates skeleton-based action recognition and local image classification to make good use of both human skeleton data and appearance information for action recognition. The experimental results demonstrate the high recognition accuracy and good generalization ability on the range hood packing dataset (RHPD) which is generated in the industrial packing process. The results can meet the recognition requirements in the actual industrial production process.

Original languageEnglish
Title of host publicationIoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security
EditorsGary Wills, Peter Kacsuk, Victor Chang
PublisherSciTePress
Pages36-45
Number of pages10
ISBN (Electronic)9789897584268
Publication statusPublished - 7 May 2020
Externally publishedYes
Event5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020 - Virtual, Online
Duration: 7 May 20209 May 2020

Publication series

NameIoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security

Conference

Conference5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020
CityVirtual, Online
Period7/05/209/05/20

Bibliographical note

Funding Information:
This work was supported by the National Natural Science Foundation of China (No. 61802095, 61572162), the Zhejiang Provincial Key Science and Technology Project Foundation (No. 2018C01012). Zhongjin Li is the corresponding author.

Publisher Copyright:
Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

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