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.
|Title of host publication||IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security|
|Editors||Gary Wills, Peter Kacsuk, Victor Chang|
|Number of pages||10|
|Publication status||Published - 25 Apr 2020|
|Event||5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020 - Virtual, Online|
Duration: 7 May 2020 → 9 May 2020
|Name||IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security|
|Conference||5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020|
|Period||7/05/20 → 9/05/20|
Bibliographical notePublisher Copyright:
Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
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.
- Image Classification
- Industrial Packing Process
- Skeleton-based Action Recognition