Crowd counting using Yolov5 and KCF

Abstract

Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. In this paper, real-time people counting is proposed using a proposed model of the YOLOv5 (You Only Look Once) algorithm and KCF (kernel correlation filter) algorithm. The YOLOv5 algorithm was used because it is considered one of the most accurate algorithms for detecting people in real time. Despite the high accuracy of the YOLOv5 algorithm in detecting the people in the image, video or real-time camera capturing, it needs an increase in speed.
For this reason, the YOLOv5 algorithm was combined with the KCF tracking algorithm. Where the YOLOv5 algorithm identifies people to be tracked by the KCF. The YOLOv5 algorithm was trained on a database of people, and the system's accuracy reached 98%. The speed of the proposed system was increased after adding the KCF.