Abstract:
This paper presents a vision-based driver drowsiness estimation system from sequences of driver images. We propose a stage-by-stage system instead of an end-to-end system...Show MoreMetadata
Abstract:
This paper presents a vision-based driver drowsiness estimation system from sequences of driver images. We propose a stage-by-stage system instead of an end-to-end system for driver drowsiness estimation. The stage-by-stage system (1) calculates features related to eyes on a frame-by-frame basis, (2) calculates temporal measures on eye states, and (3) estimates drowsiness levels by time-domain convolution with a parallel linked structure. Furthermore, we propose average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS) as novel temporal measures on eye states to extract information related to driver drowsiness. Extensive experiments have been conducted on a driving movie dataset recorded in a real car. Our system achieves a high accuracy of 95.86% and mean absolute error (MAE) of 0.4007 on the dataset.
Published in: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Date of Conference: 23-27 July 2019
Date Added to IEEE Xplore: 07 October 2019
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PubMed ID: 31946048