Face recognition for monitoring operator shift in railways
S Ritika, Dattaraj Rao

TL;DR
This paper presents an automated facial recognition system using deep learning to monitor train operators' shifts in real-time, enhancing safety and compliance in railway operations.
Contribution
It introduces a novel camera-based system that continuously records and analyzes inside-locomotive video feeds to detect and recognize train drivers for shift monitoring.
Findings
Effective real-time face detection and recognition in locomotive cabs.
Accurate tracking of shift durations to prevent over-tiring.
Automated alerts for shift violations improve operational safety.
Abstract
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.…
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Taxonomy
TopicsIoT and GPS-based Vehicle Safety Systems · Video Surveillance and Tracking Methods · Hand Gesture Recognition Systems
