Abstract
We present a vehicle color classification method from outdoor vehicle images. Although the vehicle color recognition is important especially for the newest applications including ITS (intelligent transportation system), we have no significant previous results at least to our knowledge. In this paper, we started from converting the vehicle image into an HSV(hue-saturation-value) color model-based image, to eliminate distortions due to the intensity changes. Then, we construct the feature vector, which is a two-dimensional histogram for the hue and saturation pairs. We use the SVM(support vector machine) method to classify these feature vectors into five vehicle color classes: black, white, red, yellow and blue. Our implementation result shows 94.92% of success rate for 500 outdoor vehicle images.
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Baek, N., Park, SM., Kim, KJ., Park, SB. (2007). Vehicle Color Classification Based on the Support Vector Machine Method. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_127
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DOI: https://doi.org/10.1007/978-3-540-74282-1_127
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