AI-Powered Safety Monitoring on Metro Platforms with Computer Vision Integration

Abstract

The urgent need for improved safety measures arises from the rising number of accidents and deaths on metro platforms, particularly in view of crowding in recent years. This paper examines how computer vision (CV) and artificial intelligence (AI) might assist metro stations' real-time safety monitoring to be improved. It centres on how advanced artificial intelligence models—such as deep learning and pose estimation models—may identify safety concerns, including people crossing safety lines (yellow lines), falling by accident, or exhibiting indicators of suicide attempts. According to the studies gathered for this review, computer vision can monitor platform activity constantly, identify dangerous behaviour, and alert others to stop mishaps. Human posture and motions are also studied using machine learning, which produces better safety predictions than conventional cameras. AI and Internet of Things (IoT) technologies together simplify data processing and site-based quick decision making. This paper also examines how robots might support safety by means of automated barriers, crisis assistance, and adherence to AI-based safety guidelines. These technologies taken together indicate a time when public transport will have smarter and more automatic safety systems. The paper summarises the most recent advancements, addresses present issues in the field, and offers ideas for next studies on artificial intelligence-based safety systems for metro platforms.