Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art
It addresses public safety concerns for governments and security agencies by reviewing existing approaches, but is incremental as it synthesizes rather than introduces new methods.
The paper analyzes vision-based and non-vision-based crowd-monitoring systems, focusing on the integration of AI algorithms to enhance security and safety in public gatherings, but does not report specific results or numbers.
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.