DocumentCode :
124465
Title :
Sober-Drive: A smartphone-assisted drowsy driving detection system
Author :
Lunbo Xu ; Shunyang Li ; Kaigui Bian ; Tong Zhao ; Wei Yan
Author_Institution :
Sch. of EECS, Peking Univ., Beijing, China
fYear :
2014
fDate :
3-6 Feb. 2014
Firstpage :
398
Lastpage :
402
Abstract :
Drowsy driving, a combination of sleepiness and driving, has become a worldwide problem that often leads to tragic accidents and outcomes. Existing research findings have shown that the percentage of closure of eyelid (a.k.a PERCLOS) is an effective indicator to evaluate the driver´s drowsiness. We present the Sober-Drive system, which leverages PERCLOS for on-vehicle drowsy driving detection using smart phones. Specifically, Sober-Drive is built upon a number of indicators that are discrete-approximated from PERCLOS, blink time and blink rate, and it exploits the Neural Network to classify the eye “open/close” states. We developed a Sober-Drive prototype on Android smart phones, and we conducted extensive real-world experiments to evaluate its performance, the results of which show that Sober-Drive has a high detection rate of more than 90% for drowsy driving behaviors.
Keywords :
face recognition; gaze tracking; monitoring; neural nets; sensors; sleep; smart phones; Android smart phones; PERCLOS; accidents; blink rate; blink time; drowsy driving detection system; neural network; percentage of closure of eyelid; sleepiness; smartphone; sober drive system; Accuracy; Algorithm design and analysis; Fatigue; Neural networks; Smart phones; Training; Vehicles; drowsiness detection; eye detection; neural network; personalized training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Networking and Communications (ICNC), 2014 International Conference on
Conference_Location :
Honolulu, HI
Type :
conf
DOI :
10.1109/ICCNC.2014.6785367
Filename :
6785367
Link To Document :
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