Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with Novel Temporal Measures on Eye States
Published in The 41th IEEE International Engineering in Medicine and Biology Conference, Jul. 2019
We presents a vision-based driver drowsiness estimation system from sequences of driver images. A stage-by-stage system instead of an end-to-end system is proposed for driver drowsiness estimation. Extensive experiments have been conducted on a driving video dataset recorded in real cars. Our system achieves a high accuracy of 95.86% and the MAE of 0.4007.
Download here