Enhanced Crowd Analysis Using YOLO
Year: 2025
Type: article
Abstract: Crowd analysis plays a vital role in public safety, event management, and disaster prevention by enabling real-time monitoring and anomaly detection in densely populated areas. Deep learning-based object detection models, particularly the You Only Look Once (YOLO) family, have demonstrated remarkable efficiency in such applications. In this paper, we perform a comparative analysis of YOLOv3, YOLOv5, YOLOv7, and YOLOv8 for crowd analysis, evaluating their performance in terms of detection accuracy, inference speed, model complexity, and suitability for real-time deployment. Experiments are conducted on datasets representing varying crowd densities and occlusion levels to assess each model’s effectiveness in detecting individuals in complex environments. The results highlight the trade- offs between accuracy and computational efficiency, pro- viding insight into the optimal choice of YOLO models for different crowd-analysis scenarios.
Authors: Megha B Prathap, Scion S Vayalil, Varun Sankar R, Aparna S, Deepa A B
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6912063094
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