An embedded toolset for human activity monitoring in critical environments
作者：
Highlights：
• Monitoring compliance with safety rules is crucial in critical environments.
• Presenting a Computer Vision-based AI-assisted system to monitor human activities.
• Modular architecture for pedestrian detection, counting, distancing, and PPE detection.
• Two novel datasets of PPE detection and overall system evaluation in a real scenario.
• State-of-the-art trained models in a deployed real use-case scenario in Pisa, Italy.
摘要
•Monitoring compliance with safety rules is crucial in critical environments.•Presenting a Computer Vision-based AI-assisted system to monitor human activities.•Modular architecture for pedestrian detection, counting, distancing, and PPE detection.•Two novel datasets of PPE detection and overall system evaluation in a real scenario.•State-of-the-art trained models in a deployed real use-case scenario in Pisa, Italy.
论文关键词：Deep learning,Computer vision,Machine learning,Personal protective equipment,Counting,Homography,Embedded system
论文评审过程：Received 14 July 2021, Revised 27 January 2022, Accepted 28 March 2022, Available online 8 April 2022, Version of Record 12 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117125
