Hazem Hossam, M. Ghantous, Mohammed Abdel-Megeed Salem
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引用次数: 0
摘要
针对COVID-19的容量限制,本文实现了一种人工计数系统。它使用深度学习模型You Only Look Once version 3(YOLOv3)来检测和计算房间里的人数。该系统还可以根据是否遵守世界卫生组织(who)推荐的社交距离协议,将每个人标记为“安全”或“不安全”,并监控房间内人们之间的社交距离。为了使项目对用户友好,实现了一个图形用户界面(GUI),允许用户选择图像的来源,这些图像将被用作系统处理的输入。通过实验对系统在不同条件和不同场景下的性能进行了评估,并根据准确率、精密度和召回率等指标进行了评估。该实验的输出结果进行了详细的演示,并与类似的算法进行了比较,因为这两种算法都侧重于使用倾斜相机的图像进行人员检测。结果显示,检测和计算人数的准确率为96%。
Camera-based Human Counting for COVID-19 Capacity Restriction
In this paper, a Human Counting system is implemented for COVID-19 capacity restrictions. It was implemented using the deep learning model You Only Look Once version 3(YOLOv3) to detect and count the people in a room. The system also can monitor the social distancing between the people in the room while labeling each person as “safe” or “unsafe” depending on whether they respect the social distancing protocols that the World Health Organization recommended or not. To make the project user friendly, a Graphical User Interface (GUI) was implemented to allow the user to choose the source of their images that will be used as input to be processed by the system. An experiment was carried out to evaluate the performance of the system under different conditions and in different scenarios where the evaluation was done according to some metrics such as accuracy, precision and recall. The output results from this experiment were demonstrated in details and compared to a similar algorithm as both algorithms focused on people detection using images from an inclined camera. The results show an accuracy of 96% for detection and the number of people counted.