Gündüz Mehmet Şirin, Işık Gültekin
Department of Computer Engineering, Igdir University, Igdir, Turkey.
J Real Time Image Process. 2023;20(1):5. doi: 10.1007/s11554-023-01276-w. Epub 2023 Jan 30.
As seen in the COVID-19 pandemic, one of the most important measures is physical distance in viruses transmitted from person to person. According to the World Health Organization (WHO), it is mandatory to have a limited number of people in indoor spaces. Depending on the size of the indoors, the number of persons that can fit in that area varies. Then, the size of the indoor area should be measured and the maximum number of people should be calculated accordingly. Computers can be used to ensure the correct application of the capacity rule in indoors monitored by cameras. In this study, a method is proposed to measure the size of a prespecified region in the video and count the people there in real time. According to this method: (1) predetermining the borders of a region on the video, (2) identification and counting of people in this specified region, (3) it is aimed to estimate the size of the specified area and to find the maximum number of people it can take. For this purpose, the You Only Look Once (YOLO) object detection model was used. In addition, Microsoft COCO dataset pre-trained weights were used to identify and label persons. YOLO models were tested separately in the proposed method and their performances were analyzed. Mean average precision (mAP), frame per second (fps), and accuracy rate metrics were found for the detection of persons in the specified region. While the YOLO v3 model achieved the highest value in accuracy rate and mAP (both 0.50 and 0.75) metrics, the YOLO v5s model achieved the highest fps rate among non-Tiny models.
正如在新冠疫情中所看到的，最重要的措施之一是在人际传播的病毒中保持身体距离。根据世界卫生组织（WHO）的规定，室内空间必须限制人数。根据室内空间的大小，该区域可容纳的人数各不相同。然后，应测量室内区域的大小，并据此计算出最大容纳人数。可以使用计算机来确保在摄像头监控的室内正确应用容量规则。在本研究中，提出了一种方法来测量视频中预先指定区域的大小，并实时统计该区域的人数。根据该方法：（1）预先确定视频中一个区域的边界，（2）识别并统计该指定区域内的人员，（3）旨在估计指定区域的大小，并找出其可容纳的最大人数。为此，使用了You Only Look Once（YOLO）目标检测模型。此外，还使用了微软COCO数据集的预训练权重来识别和标记人员。在所提出的方法中分别测试了YOLO模型，并分析了它们的性能。针对指定区域内人员的检测，得出了平均精度均值（mAP）、每秒帧数（fps）和准确率指标。虽然YOLO v3模型在准确率和mAP（0.50和0.75）指标上取得了最高值，但YOLO v5s模型在非Tiny模型中取得了最高的fps率。
