Abstract
Automatic vehicle classification is an important task in Intelligent Transport System (ITS) because it allows the traffic parameter, called vehicles count by category, to be obtained. In terrestrial public roads, variants sources of information for vehicles counter by category have been used such as video, magnetic induction coil, sound sensors, temperature sensors and microwave. The use of video has increased support for traffic management due to the advantages of installation cost and a wide range of information it contains. This paper presents comparison of vehicle image classification based on edge features. Contour points number, height, width and fractal dimension are used like features. Nearest neighbor, adaptive nearest neighbor and adaptive distance are used in classification. The experimental platform is built on Matlab R2009a.
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de S. Matos, F.M., de Souza, R.M.C.R. (2012). Vehicle Image Classification Based on Edge: Features and Distances Comparison. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_84
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DOI: https://doi.org/10.1007/978-3-642-34478-7_84
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