Abstract

The most effective measure to control and prevent COVID-19 is to maintain a safe social distance. The Chinese government has previously adopted a physical social distance of 1m as a standard for safe social distance in public places such as campuses and factories and has achieved good results. Social distance has also been widely recognized as an implementation program by the World Health Organization to minimize the spread of COVID-19 in public places. Therefore, for the social safety distance risk of pedestrians during the COVID-19 epidemic, this study proposes an RT-Thread-based social safety distance risk assessment under the COVID-19 epidemic. Firstly, the pedestrian is trained with fine-tuned YOLO-Fastest for target extraction, and the pedestrian key points are extracted to treat the continuous pedestrian motion as a continuous motion of prime points to achieve tracking processing of pedestrians; secondly, the trained weight files are deployed to the RT-Thread OS engineering project through STM32CubeMX AI and RT-AK toolkit to identify the face position and detect whether a mask is worn or not. The recognition rate is 30.71% for unmasked faces and 78.84% for masked faces on the ART-Pi with a maximum main frequency of 480Mhz. It has good performance in human flow calculation and spacing calculation by pixel position as a difference. This can provide ideas for handling safe social distancing of pedestrians in places such as campuses for epidemic prevention and control.

Full Text
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