Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art

TL;DR

This paper provides a comprehensive analysis of modern crowd-monitoring systems, focusing on vision-based and non-vision-based technologies, and explores the integration of AI algorithms to enhance safety and security in public gatherings.

Contribution

It offers an in-depth review of recent advancements in AI-driven crowd-monitoring approaches, comparing their effectiveness across different environments and contexts.

Findings

AI algorithms improve crowd detection accuracy

Vision-based systems are effective in real-time monitoring

Non-vision systems offer advantages in low-light conditions

Abstract

Growing apprehensions surrounding public safety have captured the attention of numerous governments and security agencies across the globe. These entities are increasingly acknowledging the imperative need for reliable and secure crowd-monitoring systems to address these concerns. Effectively managing human gatherings necessitates proactive measures to prevent unforeseen events or complications, ensuring a safe and well-coordinated environment. The scarcity of research focusing on crowd monitoring systems and their security implications has given rise to a burgeoning area of investigation, exploring potential approaches to safeguard human congregations effectively. Crowd monitoring systems depend on a bifurcated approach, encompassing vision-based and non-vision-based technologies. An in-depth analysis of these two methodologies will be conducted in this research. The efficacy of these approaches is contingent upon the specific environment and temporal context in which they are deployed, as they each offer distinct advantages. This paper endeavors to present an in-depth analysis of the recent incorporation of artificial intelligence (AI) algorithms and models into automated systems, emphasizing their contemporary applications and effectiveness in various contexts.