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

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

Counting People in ITS Systems Based on WiFi Traffic Analysis

Tato práce se zaměřuje na zachytávání a analýzu Wi-Fi provozu s cílem odhadnout počet osob pomocí filtrování MAC adres. K tomu jsou využívány různé programy k~zachytávání Wi-Fi paketů. Zachycený provoz je pak následně analyzován pro stanovení počtu přítomných osob. Dále se výzkum zabývá výzvou filtrování falešných MAC adres generovaných zařízeními, které mohou zkreslit přesnost odhadu. Tato práce přispívá do~oblasti analýzy Wi-Fi provozu a poskytuje cenné poznatky, které budou později využity pro zlepšení dohledu davu v integrovaných veřejných dopravních systémech.This thesis focuses on capturing and analyzing Wi-Fi traffic to estimate the number of individuals using MAC address filtering. Various programs are utilized to capture Wi-Fi packets around a monitoring station, and the collected traffic is later analyzed to determine the number of people present. Additionally, the research addresses the challenge of filtering out fake MAC addresses generated by devices, which can distort the accuracy of the estimation. This research contributes to the field of Wi-Fi traffic analysis and provides valuable knowledge and will be later used for improving crowd management in public integrated transport systems.460 - Katedra informatikyvýborn