Investigating the effect of distance on the implementation of RCNN automatic detection technique to the human body

Abstract

      The identification of humans constitutes a crucial component of monitoring systems, given the significance of the timely detection of individuals. Despite advancements in people detection systems, detecting humans at long distances remains challenging. In this study, we employed the Region-based Convolutional Neural Network (RCNN) approach to training a system on images captured at varying distances between the camera and individuals. The results demonstrate promising outcomes, with the system achieving a maximum detection recall of 1 for identifying people at distances of up to 40 meters and maximum precision of 1 for identifying people at distances of up to 50 meters.