Learning to estimate crowd size by applying convolutional neural network to Aerial Imaging Analysis

Abstract

Abstract: Using image and video to conduct crowd analysis in public places is an effective tool to establish situational awareness. Currently, the gap between different organizations on crowd counting differs greatly. Many research works investigated into utilizing image recognition technology to provide a fair estimation of the crowd count. In this paper, we propose a convolutional neural network model on aerial image analysis to learn to estimate crowd size. To find out the requirements of the efficient and reliable crowd size estimation system, we also investigate current approaches in crowd size estimation, such as regression, CNN and by-detention with image recognition technology. Our work allows the event organizers to get a fair description of the crowd behaviors. The main contribution of this paper is the application of CNN for solving the problem of crowd size estimation.