Three-Layer Single Convolutional Neural Network Model for Estimating Crowd Density

Abstract

The estimation of crowd density is a crucial area of study in computer vision because of its extensive use in intelligence collection, urban planning, and surveillance. The crowd density prediction that comes from a thorough analysis considers several factors, including inter-blocking in dense crowds, background elements, and individual appearance similarity. To monitor populous areas and avoid congestion, we are interested in using machine learning for crowd control in this research. We suggest using a Single Convolutional Neural Network with Three Layers (S-CNN3) model to estimate the crowd size and count the number of individuals in a scene. Next, a comparison study for density counting determines how well the suggested model performs in comparison to switched convolutional neural networks (SCNN) and convolutional neural networks with four layers (single CNN4). This study makes use of the Shanghai Tech dataset, which is regarded as the biggest database for crowd counting. With an average test accuracy of 99.88% and an average validation loss of 0.02 for crowd density estimate, the suggested model demonstrates remarkable efficacy and efficiency. These outcomes outperform the state-of-the-art models already in use.