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Conference Paper Vehicle Color Recognition via Representative Color Region Extraction and Convolutional Neural Network
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Authors
Kwang-Ju Kim, Pyong-Kun Kim, Kil-Taek Lim, Yun-Su Chung, Yoon-Jeong Song, Soo In Lee, Doo-Hyun Choi
Issue Date
2018-07
Citation
International Conference on Ubiquitous and Future Networks (ICUFN) 2018, pp.89-94
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICUFN.2018.8436710
Project Code
18ZD1100, Development of ICT Convergence Technology for Daegu-GyeongBuk Regional Industry, Moon Ki Young
Abstract
Vehicle color recognition is one of the important part in ITS (Intelligent Transportation System). This paper presents a new vehicle color classification technique for CCTV systems via representative color region extraction and Convolutional Neural Net (CNN). The Harris corner point detection method is used to generate a probability map of a representative color region. From the probability map, point are randomly selected to generate an input image for CNN. Finally, we trained CNN model with it. In order to evaluate the performance of the proposed method, we acquired a total of 5,941 images from camera on highway. We conducted 5-fold cross validation for performance evaluation. Our vehicle color recognition method performance of about 96.1 % was shown.
KSP Keywords
5-Fold cross validation, CCTV systems, CNN model, Classification techniques, Convolution neural network(CNN), Convolutional neural net, Cross validation(CV), Detection Method, Harris corner point detection, Intelligent Transport Systems(ITS), Method performance