AI-Based Security Surveillance and Hazard Detection for Train Platform Safety

Safety in train platforms is a common concern in railway systems around the world. There are grave risks associated with the tracks and the crowds that often end up in injuries or deaths. Traditional approaches for ensuring safety in train transport networks rely on passive closed-circuit television (CCTV) monitorization that needs constant human attention. This paper proposes an AI-based alternative for detecting anomalies in surveillance videos, that is more efficient and cost-effective than traditional methods. Through Video Instance Segmentation (VIS), this work detects common risks such as overcrowding on the platform, people or objects standing on the edge of the platform, individuals or objects falling onto the tracks, the presence of firearms, and the presence of unattended baggage. The proposed algorithm combines state-of-the-art models like YOLOv8, ByteTrack, and Segment Anything Model (SAM) to classify, track, and segment object detections respectively. Additionally, this paper presents a custom-trained YOLOv8 model for gun detection. The results show that the system can successfully analyze video, create surveillance annotations, detect hazardous situations to alert authorities, and help prevent accidents and incidents on train platforms.