Overview of Deep Learning-Based Approaches for Human Image Classification and Detection in Mass Crowds

Abstract

Applications include crowd monitoring, public safety, and behavioural analysis, made possible by the widespread use of deep learning, which has transformed human image classification and detection in large-crowd scenarios. With an emphasis on convolutional neural networks (CNNs), object detection frameworks such as YOLO and Faster R-CNN, and sophisticated architectures that integrate attention mechanisms and spatiotemporal analysis, this paper offers a thorough overview of recent deep learning-based techniques for identifying and categorising people in dense crowds. We highlight cutting-edge methods and their performance metrics while discussing important issues such as occlusions, fluctuating crowd densities, and real-time processing requirements. Furthermore, we propose a novel Density-Aware Attention Network (DAAN) that improves detection accuracy in dense crowds. In addition, the study discusses ethical issues like bias and privacy and suggests future paths of inquiry.