@inproceedings{622ce34e224a49c9af6463fe1a68b601,
title = "Vehicle Color Recognition via Representative Color Region Extraction and Convolutional Neural Network",
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.",
keywords = "CNN, Color Recognition, Probability Map, Vehicle",
author = "Kim, \{Kwang Ju\} and Kim, \{Pyong Kun\} and Lim, \{Kil Taek\} and Chung, \{Yun Su\} and Song, \{Yoon Jeong\} and Lee, \{Soo In\} and Choi, \{Doo Hyun\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th International Conference on Ubiquitous and Future Networks, ICUFN 2018 ; Conference date: 03-07-2018 Through 06-07-2018",
year = "2018",
month = aug,
day = "14",
doi = "10.1109/ICUFN.2018.8436710",
language = "English",
isbn = "9781538646465",
series = "International Conference on Ubiquitous and Future Networks, ICUFN",
publisher = "IEEE Computer Society",
pages = "89--94",
booktitle = "ICUFN 2018 - 10th International Conference on Ubiquitous and Future Networks",
address = "United States",
}