In this paper, the problem of ensuring automative security control of the subway tunnel is considered. To achieve that, we design and implement an automative video analysis system which automatically detects various relevant events in video sequences. To detect events, the system uses classical computer vision algorithms, such as local image features and connected components analysis. To minimize false event detections and to consider typical and expected scene changes, the system uses integration of individual responses between frames. The event detection quality was measured on the real data of videoclips captured during on the subway tunnel during technical hours, which includes railways inspections by technicians and technical trains passage, and usual subway opening hours. The total precision and recall of event detection in these videos equals to 91.49% and 97.73% respectively. The offline system, designed to optimize the use of computing resources of the device, provides performance at 186 frames per second on the Odroid single-board computer. The proposed method has demonstrated high quality and performance and can be applied on devices with even lower computation power or to ensure subway tunnel security from several video sources simultaneously. The design of the final system is scalable and allows to adapt its individual components to detect events to ensure the security of infrastructure facilities.