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


Wook-Sun Shin, Doo-Heon Song, Chang-Hun Lee, Journal of Information Processing Systems Vol. 2, No. 1, pp. 52-57, Feb. 2006


Keywords: Vehicle Type classification, Road Lane Detection, Model fitting, Vanishing point, Machine Learning
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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.


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Cite this article
[APA Style]
Wook-Sun Shin, Doo-Heon Song, & Chang-Hun Lee (2006). Vehicle Classification by Road Lane Detection and Model Fitting Using a Surveillance Camera. Journal of Information Processing Systems, 2(1), 52-57. DOI: .

[IEEE Style]
W. Shin, D. Song and C. Lee, "Vehicle Classification by Road Lane Detection and Model Fitting Using a Surveillance Camera," Journal of Information Processing Systems, vol. 2, no. 1, pp. 52-57, 2006. DOI: .

[ACM Style]
Wook-Sun Shin, Doo-Heon Song, and Chang-Hun Lee. 2006. Vehicle Classification by Road Lane Detection and Model Fitting Using a Surveillance Camera. Journal of Information Processing Systems, 2, 1, (2006), 52-57. DOI: .