مجلة الاربعين المحكمة
Understanding how people behave in crowded places is an important endeavor with several uses, like controlling the spread of COVID-19 and boosting security. In-depth study of crowd scene analysis methods, including both crowd counting and crowd activity detection, is included in this survey article. This article fills the gap by exhaustively examining the spectrum up to contemporary deep learning techniques, whereas current studies frequently focus primarily on certain aspects or traditional approaches. Paper proposes the innovative idea of Crowd Divergence (CD) evaluation as a matrix for evaluating crowd scene analysis approaches, which was motivated by information theory. Contrary to conventional measurements, CD quantifies the agreement between expected and observed crowd count distributions. This paper makes three key contributions: an examination of readily available crowd scene datasets, the use of CD for thorough technique evaluation, and a thorough examination of crowd scene methodologies. The investigation starts with conventional computer vision methods, closely examining density estimates, detection, and regression strategies. Convolutional neural networks (CNNs) become effective tools as deep learning progresses, as seen by new models like ADCrowdNet and PDANet, which make use of attention mechanisms and structured feature representation. To evaluate algorithmic effectiveness, a variety of benchmark datasets including ShanghaiTech, UCF CC 50, and UCSD are carefully examined. Computer vision's exciting and challenging topic of "crowd scene analysis" has numerous