  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.
