Optimization of an Artificial Intelligence Database and Camera Installation for Recognition of Risky Passenger Behavior in Railway Vehicles

Abstract

Urban railways are vital means of public transportation in Korea. More than 30% of metropolitan residents use the railways, and this proportion is expected to increase. To enhance safety, the government has mandated the installation of closed-circuit televisions in all carriages by 2024. However, cameras still monitored humans. To address this limitation, we developed a dataset of risk factors and a smart detection system that enables an immediate response to any abnormal behavior and intensive monitoring thereof. We created an innovative learning dataset that takes into account seven unique risk factors specific to Korean railway passengers. Detailed data collection was conducted across the Shinbundang Line of the Incheon Transportation Corporation, and the Ui-Shinseol Line. We observed several behavioral characteristics and assigned unique annotations to them. We also considered carriage congestion. Recognition performance was evaluated by camera placement and number. Then the camera installation plan was optimized. The dataset will find immediate applications in domestic railway operations. The artificial intelligence algorithms will be verified shortly.