In this article, it is aimed to graphically represent human communities in certain regions and evaluate related control measures by examining crowd counting methods. Today, crowd management is an important issue in terms of security, order, and efficiency, especially in areas with large events and heavy human traffic. In this context, first the theoretical foundations of image processing and artificial intelligence-based approaches will be discussed, and then how more accurate and faster predictions can be made with the data used in crowd counting will be investigated. In the experimental phase, tests will be carried out on different environmental conditions and camera angles, and the accuracy rates and possible error sources of the obtained results will be discussed. Finally, through the visualization of crowd data and the creation of regional density maps, necessary strategic recommendations will be presented so that security officers and relevant authorities can make rapid and effective interventions. This study aims to contribute to the development of more effective practices in crowd management and safety.