Published Paper Details:

Crowd analysis and management research has evolved as an important subject for solving public safety issues, urban planning, and event management. In 1895, Gustave Le Bon published The Crowd: A Study of the Popular Mind, which marked the beginning of the study of crowds and their behavior [39], and since then several researches have emerged on this topic. Researching crowd behavior is still one of the most fascinating subjects available today. Overcrowding is one of the most serious issues created by a large crowd. Public area overcrowding frequently results in safety risks, ineffective logistics, and diminished personal experiences. Effective systems that are able to track, evaluate, and forecast crowd behavior in real time are required due to the growing frequency and size of gatherings. In dynamic contexts, conventional techniques like static surveillance and manual monitoring have not worked well. Therefore, utilizing developments in neural networks and image processing presents revolutionary opportunities for automated crowd analysis. The most recent approaches and systems that use deep learning and image processing methods for crowd control are examined in this literature survey