Driver Distraction Detection using Single Convolutional Neural Network

被引：0

作者：

Kim, Whui ^{[1
]}

Choi, Hyun-Kyun ^{[1
]}

Jang, Byung-Tae ^{[1
]}

Lim, Jinsu ^{[2
]}

机构：

[1] Elect & Telecommun Res Inst, Intelligent Robot Res Div, Daejeon, South Korea

[2] Chungnam Natl Univ, Dept Comp Sci & Engn, Daejeon, South Korea

来源：

2017 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC) | 2017年

关键词：

Inception Resnet; MobileNet; Driver Distraction Detection; Driver Status Detection;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：1203 / 1205

页数：3

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