Anaomaly Detection at Crowded Places

As research has indicated, the population is tremendously increasing and the numbers are expected to reach approximately 9.7 billion by the year 2050, a significant increase from the 7.4 billion recorded in 2016. This continuous population growth has underscored the critical importance of crowd detection, making it a pivotal tool in a wide array of studies and applications, including crowd counting, urban planning, crime detection, and visual surveillance. This combination of capabilities opens the door to numerous novel studies and applications. For instance, crowd density detection becomes invaluable during times of pandemics, such as the COVID-19 crisis. It allows for the proactive monitoring and management of overcrowding situations, ensuring compliance with safety regulations, and maintaining proper social distancing measures. The primary objective of crowd density detection is to identify and quantify the congregation of individuals within video footage, subsequently recognizing discernible patterns and generating output results based on these observed patterns. Enhancing accuracy in crowd detection involves incorporating various complementary techniques alongside Convolutional Neural Networks (CNN). These methods include applying Haar filters to video frames and integrating Multi-Scale Convolutional Neural Networks (MSCNNs). The choice of approach for crowd detection and classification depends on the crowd’s density being observed. In densely populated areas, the focus often shifts away from individual face detection towards analyzing crowd behavior and tracking crowd movements. This review paper provides a comprehensive analysis of crowd detection and classification, encompassing a range of general techniques for crowd analysis and monitoring.