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http://dx.doi.org/10.3745/JIPS.2006.2.1.052

Vehicle Classification by Road Lane Detection and Model Fitting Using a Surveillance Camera  

Shin, Wook-Sun (Dept. of Computer Science, Konkuk University)
Song, Doo-Heon (Dept. of Computer Games & Information, Yong-in Songdam College)
Lee, Chang-Hun (Dept. of Computer Science, Konkuk University)
Publication Information
Journal of Information Processing Systems / v.2, no.1, 2006 , pp. 52-57 More about this Journal
Abstract
One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.
Keywords
Vehicle Type classification; Road Lane Detection; Model fitting; Vanishing Point; Machine Learning;
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