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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks

Computer Engineering Department, Fatih University Đstanbul, Turkey.
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Math. Comput. Appl. 2009, 14(3), 187-196; https://doi.org/10.3390/mca14030187
Published: 1 December 2009
It is important to know the road traffic density real time especially in mega cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. In most vehicle detection methods in the literature, only the detection of vehicles in frames of the given video is emphesized. However, further analysis is needed in order to obtain the useful information for traffic management such as real time traffic density and number of vehicle types passing these roads. This paper presents vehicle classification and traffic density calculation methods using neural networks. The paper also reports results from real traffic videos obtained from Istanbul Traffic Management Company (ISBAK).
Keywords: Vehicle Identification; Motion Detection; Traffic Density Estimation Vehicle Identification; Motion Detection; Traffic Density Estimation
MDPI and ACS Style

Ozkurt, C.; Camci, F. Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks. Math. Comput. Appl. 2009, 14, 187-196.

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