Deep Learning Approaches for Crowd Density Estimation: A Review

The term “crowd density estimation” refers to the process of determining the number of people in a certain region or a specific area. This method has a wide range of applications, including urban planning, healthcare, emergency response, public safety, and strategic planning. Occlusion, size and perspective distortion, and uneven distribution all pose problems for crowd counting systems. Calculations grow increasingly complex as population density increases. The considerable contributions of deep convolutional neural networks (CNNs) and developments in datasets have contributed to the notable progress in crowd counting approaches in recent years. This study examines both standard and deep learning-based crowd counting approaches in depth. It investigates detection-based, regression-based, and traditional density estimate methods. The 10 most recent works on crowd counting using deep learning are reviewed, with an emphasis on estimating crowd density and count from available photos. The research also looks at regularly utilized datasets. The findings of these investigations indicate a high level of precision, demonstrating the promise of AI in crowd counting. Notably, the approaches and algorithms used to handle the issues of crowd counting and density mapping vary significantly between articles. The research investigates prospective uses of crowd counting as well as the accompanying obstacles. Various crowd counting models and methodologies are examined, stressing the range of approaches to dealing with the problems of crowd counting and density mapping.