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ON-ROAD VEHICLE CLASSIFICATION BASED ON RANDOM NEURAL NETWORK AND BAG-OF-VISUAL WORDS

Published online by Cambridge University Press:  18 May 2016

Khaled F. Hussain  and
Ghada S. Moussa
Khaled F. Hussain
Affiliation:
Faculty of Computers and Information, Assiut University, Assiut, Egypt E-mail: [email protected]
Ghada S. Moussa
Affiliation:
Civil Engineering Department, Assiut University, Assiut, Egypt E-mail: [email protected]
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Abstract

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A large increase in the number and types of vehicles occurred due to the growth in population. This fact brings the need for efficient vehicle classification systems that can be used in traffic surveillance and intelligent transportation systems. In this study, a multi-type vehicle classification system based on Random Neural Networks (RNNs) and Bag-Of-Visual Words (BOVWs) is developed. A 10-fold cross-validation technique is used, with a large dataset, to assess the proposed approach. Moreover, the BOVW–RNN's classification performance is compared with LIVCS, a vehicle classification system based on RNNs. The results reveal that BOVW–RNN classification system produces more reliable and accurate classification results than LIVCS. The main contribution of this paper is that the developed system can serve as a framework for many vehicle classification systems.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 30 , Issue 3: Erol Gelenbe's 70th Birthday , July 2016 , pp. 403 - 412
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © Cambridge University Press 2016

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