A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models

Abstract

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.

Keywords: Area estimation; Deep learning; People counting; Person detection; Real-time video processing; YOLO.

Fig. 1

YOLO v4 architecture [15]

Fig. 2

Main structure of YOLO v4 architecture [28]

Fig. 3

Structure comparison between a Standard DenseNet and b CSPDenseNet [29]

Fig. 4

Determination of a region as desired

Fig. 5

Area calculation algorithm for a specified region in a video

Fig. 6

The proposed method to estimate the number of people in a specified region

Fig. 7

Samples taken from the experimental environment: a 1024 × 768 pixels and b 800 × 600 pixels

Fig. 8

a Number of persons detected in the region $<$ The max capacity. b Number of persons detected in the region $>$ The max capacity. Note that the area at the top (a) is larger than the area at the bottom (b)