Issue |
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
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Article Number | 03024 | |
Number of page(s) | 6 | |
Section | Computing Methods and Computer Application | |
DOI | https://doi.org/10.1051/matecconf/202235503024 | |
Published online | 12 January 2022 |
Behavior monitoring model of kitchen staff based on YOLOv5l and DeepSort techniques
National Engineering Laboratory for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing, 100048, China
* Corresponding author: zuomin@btbu.edu.cn
Although the monitoring system has been widely used, the actual monitoring task still needs more manpower to complete. This paper takes yolov5l model and deep sort algorithm as the basic framework to identify and track the staff in kitchen environment. We apply a relation construction with detected items and people, then label the relation corresponding to behaviors violate the regulations of kitchen, such as the staff did not wear mask or hat. We train our model and the experimental results show that the model can correctly identify the inappropriate behaviors of staff. The model achieves the time-constrained accuracy of 95.32% in identifying whether the staff wear a hat or not, and the time-constrained accuracy of 96.32% in identifying whether the staff wear mask correctly. The result shows that the proposed model could fulfil monitoring task in this kitchen environment.
Key words: Object detection / YOLOv5l model / DeepSort / Compressed deep learning model / Automation
© The Authors, published by EDP Sciences, 2022
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