Face recognition for monitoring operator shift in railways
This addresses safety and regulatory compliance issues for railroads by ensuring pilots adhere to mandated shift limits, though it is an incremental application of existing technology to a specific domain.
The paper tackles the problem of monitoring train pilot shifts to prevent fatigue by proposing an automated camera system that uses deep learning for face recognition to detect and identify drivers in real time, resulting in increased safety through timely alerts when shifts exceed the allocated time.
Train Pilot is a very tedious and stressful job. Pilots must be vigilant at all times and its easy for them to lose track of time of shift. In countries like USA the pilots are mandated by law to adhere to 8 hour shifts. If they exceed 8 hours of shift the railroads may be penalized for over-tiring their drivers. The problem happens when the 8 hour shift may end in middle of a journey. In such case, the new drivers must be moved to the location locomotive is operating for shift change. Hence accurate monitoring of drivers during their shift and making sure the shifts are scheduled correctly is very important for railroads. Here we propose an automated camera system that uses camera mounted inside Locomotive cabs to continuously record video feeds. These feeds are analyzed in real time to detect the face of driver and recognize the driver using state of the art deep Learning techniques. The outcome is an increased safety of train pilots. Cameras continuously capture video from inside the cab which is stored on an on board data acquisition device. Using advanced computer vision and deep learning techniques the videos are analyzed at regular intervals to detect presence of the pilot and identify the pilot. Using a time based analysis, it is identified for how long that shift has been active. If this time exceeds allocated shift time an alert is sent to the dispatch to adjust shift hours.