A comprehensive survey On Real Time Crowd Detection And Management Using Vineland Social Maturity Scale : Deep Learning Study

Abstract

Nowadays crowd Management is big issue for public safety. By using deep learning methodology the behavior of big crowd can be managed easily. The goal of the paper is to review deep learning methodologies for crowd detection and management. The system involves the use of cameras, sensors, and other technologies to monitor and analyze the movement and behavior of large groups of people in public spaces, such as airports, railway station, stadiums, and shopping malls. The goal of crowd detection and management is to improve safety, security, and efficiency in these spaces by identifying potential crowd- related problems, such as congestion, stampedes, and suspicious behavior, and providing timely updates to prevent or mitigate them. The objective of this study is to survey a existing system which will be Effective solution for crowd detection and management. To build this application requires a multidisciplinary approach that integrates knowledge from computer science, engineering, psychology, and sociology .In this paper existing deep learning methodology Vineland Social Maturity Scale (VSMS) approach can be used or implementation of the application which will be used for public safety, event planning and public transportation. This paper is organized in different sections. Section 1 consists of Introduction. Section 2 gives Literature Survey of the research papers which are referred for study. Section 3 consists of algorithms used in papers, Section 4 is proposed architecture of the system. Sec-tion 5 is Implementation framework. Section 6 is Methodology.