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Conference Paper
Driver Distraction Detection using Single Convolutional Neural Network
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- Authors
- Whui Kim, Hyun-Kyun Choi, Byung-Tae Jang, Jinsu Lim
- Issue Date
- 2017-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2017, pp.1204-1206
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Convolution neural network(CNN), Distraction detection, Driver distraction, Driver status, Status detection, Traffic accident, neural network model