Driver distraction detection using single convolutional neural network

Driver status detection is an essential task because driver distraction, fatigue, and drowsiness of driver are serious causes of traffic accident in recent. In this paper, we focus on driver distraction and propose a method to detect driver distraction. We detect driver distraction using single Convolutional Neural Network model such as Inception ResNet and MobileNet. As our experiments, both models can be trained with a small amount of dataset and checkpoints which were pre-trained with ILSVRC2012 dataset. Furthermore, although our training dataset consists images of two subjects, our method shows reliable result for other subjects.

[1]  In-Ho Choi,et al.  Tracking a Driver’s Face against Extreme Head Poses and Inference of Drowsiness Using a Hidden Markov Model , 2016 .

[2]  Yu Qiao,et al.  Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , 2016, IEEE Signal Processing Letters.

[3]  Kang Ryoung Park,et al.  Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes , 2015 .

[4]  Jeongdan Choi,et al.  Parking Space Recognition for Autonomous Valet Parking Using Height and Salient-Line Probability Maps , 2015 .

[5]  Ig-Jae Kim,et al.  Detecting driver drowsiness using feature-level fusion and user-specific classification , 2014, Expert Syst. Appl..

[6]  Marios Savvides,et al.  Multiple Scale Faster-RCNN Approach to Driver’s Cell-Phone Usage and Hands on Steering Wheel Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  Yuen Kevan,et al.  Looking at faces in a vehicle: A deep CNN based approach and evaluation , 2016 .

[8]  Hye Sun Park,et al.  Adaptive Multimodal In‐Vehicle Information System for Safe Driving , 2015 .

[9]  Samyeul Noh,et al.  Co‐Pilot Agent for Vehicle/Driver Cooperative and Autonomous Driving , 2015 .

[10]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Fei Pan,et al.  Driver Drowsiness Detection System Based on Feature Representation Learning Using Various Deep Networks , 2016, ACCV Workshops.

[13]  Jae Hong Ryu,et al.  Multimodal Interface Based on Novel HMI UI/UX for In‐Vehicle Infotainment System , 2015 .

[14]  Byoung-Jun Park,et al.  Effects of Augmented-Reality Head-up Display System Use on Risk Perception and Psychological Changes of Drivers , 2016 .

[15]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.