A BENCHMARK DATASET AND COMPARATIVE STUDY OF DEEP LEARNING MODELS FOR PEDESTRIAN HEAD DETECTION IN CROWDED SCENES

Abstract

The automatic detection of pedestrian heads in crowded environments is essential for enhancing public safety and enabling efficient crowd management, particularly in sensitive areas such as railway platforms and event entrances. These environments, often characterized by high density, occlusion, and complex visual conditions, remain underrepresented in existing public datasets. To address this challenge, we introduce the Railway Platforms and Event Entrances-Heads (RPEE-Heads) dataset, a diverse collection of high-resolution images with carefully annotated head regions. Each annotation provides precise bounding boxes around visible pedestrian heads, facilitating the training and evaluation of advanced detection models. In this study, we employ the YOLOv8 architecture to demonstrate the effectiveness of the dataset for real-time head detection. The RPEE-Heads dataset serves as a valuable benchmark for advancing research in computer vision and surveillance, particularly in applications focused on safetycritical crowd analysis and monitoring. Index Terms – Pedestrian Head Detection, Benchmark Dataset, Deep Learning, Crowded Scenes, Object Detection, Computer Vision, Crowd Analysis, Surveillance, CNN, Artificial Intelligence.