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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
- 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