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http://dx.doi.org/10.14372/IEMEK.2017.12.2.113

Night-time Vehicle Detection Method Using Convolutional Neural Network  

Park, Woong-Kyu (Yeungnam University)
Choi, Yeongyu (Yeungnam University)
KIM, Hyun-Koo (Yeungnam University)
Choi, Gyu-Sang (Yeungnam University)
Jung, Ho-Youl (Yeungnam University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.12, no.2, 2017 , pp. 113-120 More about this Journal
Abstract
In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.
Keywords
Night-time vehicle detection; Convolutional neural network; Head-lamp detection; Rear-lamp detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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