AI Powered Crowd Management: A Survey

Authors:

Shravya Prakash, S V Navya, Yashaswini.S, Shrusti.L

Department of Computer Science and Engineering, K.S. Institute of Technology #14, Raghuvanahalli, Kanakapura Main Road, Bengaluru - 560109

Guide: Mrs. Kodur Srividya

Assistant Professor, Department of Computer Science and Engineering, K.S. Institute of Technology #14, Raghuvanahalli, Kanakapura Main Road, Bengaluru - 560109

ABSTRACT:

The rapid growth of artificial intelligence and computer vision has enabled efficient real-time analysis of crowd behavior, making automated crowd monitoring an essential research area in modern urban environments. With increasing population density and large public gatherings, traditional surveillance systems face challenges in handling complex scenarios such as occlusion, scale variation, and dynamic crowd movement.

This survey presents a comprehensive review of deep learning-based crowd analysis techniques, including crowd counting, density estimation, behavior recognition, and real-time object detection. Models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), hybrid architectures, and YOLO-based frameworks are analyzed for their effectiveness in crowd understanding tasks. In particular, real-time detection systems like YOLO (You Only Look Once) demonstrate strong performance in fast and accurate crowd monitoring.

The survey also discusses key challenges, including computational complexity, poor generalization across diverse environments, and real-time processing limitations in dense crowds. Comparative analysis indicates that hybrid models and YOLO-based approaches offer improved performance for real-time applications. Finally, the paper highlights future research directions such as lightweight architectures, transformer-based models, and enhanced generalization for real-world deployment.

Key Words: Crowd Analysis, Deep Learning, CNN, YOLO, Density Estimation, Crowd Monitoring, Object Detection