Abstract detail

377 / 2021-11-24 16:28:19
Abnormal action recognition in pharmaceutical workshop based on human key points
deep learning,human key points estimation,abnormal action
Other related fields
Draft Paper Accepted
Zhongyong Chen / Zhejiang Medical Products Information Publicity and Development Service Center
Hang Xu / Zhejiang Medical Products Information Publicity and Development Service Center
Gang Wang / Zhejiang Medical Products Information Publicity and Development Service Center
Luyao Zhou / Zhejiang Medical Products Information Publicity and Development Service Center
Yuxing Wang / Zhejiang University
The abnormal action such as smoking in pharmaceutical workshop is often accompanied by immense potential dangers. Therefore, it is of great significance to detect and warn abnormal action during normal operation. With the improvement of human pose estimation algorithms, action analysis approaches based on human key points have been proposed, which could be utilized to identify various actions with high accuracy. In this paper, the most advanced human pose estimation algorithm HRNet is adopted to extract human key points for modelling analysis, which is used to establish a library of different actions. Afterwards, the template matching method is used to detect abnormal actions. Three different actions namely hat removal, smoking and non-abnormal actions are take into consideration for model performance evaluation. The final results show that the abnormal action analysis based on human key points has higher stability and reaches satisfactory accuracy of 82.8%.

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Important Dates

Abstract Submission Deadline:

 31st March 2021 15th April 2021

Extended Deadline: 1st Aug. 2022

 

Abstract Acceptance:

30th April  2021 Rollover

 

Full Paper Submission Deadline:

30th June 2021  14th July 2021

Extended Deadline: 15th Aug. 2022 

 

Notification of Acceptance:

15th August 2021 1st Sept. 2021

1st Sept. 2022

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