A review on the use of top-view surveillance videos for pedestrian detection, tracking and behavior recognition across public spaces

Abstract

The use of top-view surveillance cameras has been considered as the feature to maintain uncovered view and privacy protection in public buildings like stations and traffic hubs. This study aims to provide a comprehensive review on recent developments and challenges related to the use of top-view surveillance videos in public places. The techniques using top-view images in pedestrian detection, tracking and behavior recognition are reviewed, specifically focusing on their influence on crowd control and safety management. The setup of top-view cameras and the characteristics of several available datasets are introduced. The methodologies, field of view, extracted features, region of interest, color space and used datasets for key literature are consolidated. This study contributes by identifying key advantages of top-view cameras, such as their ability to reduce occlusions and preserve privacy, while also addressing limitations, including restricted field of view and the challenges of adapting algorithms to this unique perspective. We highlight knowledge gaps in leveraging top-view cameras for transport hubs, such as the need for advanced algorithms and the lack of standardized datasets for dynamic crowd scenarios. Through this review, we aim to provide actionable insights for improving crowd management and safety measures in public buildings, especially transport hubs.