International Journal of Contemporary Research in Multidisciplinary

Studying how people behave in crowded places is crucial for controlling the spread of diseases like COVID-19. This survey article presents an in-depth examination of crowd scene analysis methods, including crowd counting and crowd activity detection. It covers the spectrum up to contemporary deep learning techniques, which are often overlooked in current studies that focus mainly on traditional approaches or specific aspects. 

The article introduces the innovative concept of Crowd Divergence (CD) evaluation, which is a matrix for evaluating crowd scene analysis methods based on information theory. CD quantifies the agreement between expected and observed crowd count distributions, unlike traditional measurements. The paper makes three significant contributions: examining available crowd scene datasets, using CD for a thorough evaluation of techniques, and providing a comprehensive review of crowd scene methodologies. 

The investigation begins with conventional computer vision methods, including density estimates, detection, and regression strategies. As deep learning advances, convolutional neural networks (CNNs) become effective tools, as demonstrated by new models like ADCrowdNet and PDANet, which use attention mechanisms and structured feature representation. A variety of benchmark datasets, such as ShanghaiTech, UCF CC 50, and UCSD, are analyzed to evaluate algorithmic effectiveness. 

Crowd scene analysis is a fascinating and challenging topic in computer vision, with numerous applications ranging from crowd control to security surveillance. This survey article offers a comprehensive overview of crowd scene analysis, bringing together multiple approaches under a single heading and presenting the CD measure to ensure reliable evaluation. It provides a complete resource for researchers and practitioners through a detailed investigation of methods, datasets, and cutting-edge evaluation techniques, paving the way for improved crowd scene analysis techniques in various fields.