Skeleton-based action recognition for industrial packing process


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.

Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: Publisher Copyright: Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. 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.
Event Title: 5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020
Event Type: Other
Event Dates: 2020-05-07 - 2020-05-09
Uncontrolled Keywords: Image Classification,Industrial Packing Process,Skeleton-based Action Recognition,Software,Computer Networks and Communications
ISBN: 9789897584268
Full Text Link: https://www.sci ... 93408/93408.pdf
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.ins ... onDetails/93408 (Organisation URL)
https://www.sci ... WusMrhdOt4=&t=1 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2020-04-25
Authors: Chen, Zhenhui
Hu, Haiyang
Li, Zhongjin
Qi, Xingchen
Zhang, Haiping
Hu, Hua
Chang, Victor (ORCID Profile 0000-0002-8012-5852)

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