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Volume: 28 | Article ID: art00006
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Vehicle type recognition using multiple-feature combinations
  DOI :  10.2352/ISSN.2470-1173.2016.3.VSTIA-515  Published OnlineFebruary 2016
Abstract

This paper proposes a real-time vehicle tracking and type recognition system. An object tracker is recruited to detect vehicles within CCTV video footage. Subsequently, the vehicle region-of-interest within each frame are analysed using a set of features that consists of Region Features, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) histogram features. Finally, a Support Vector Machine (SVM) is recruited as the classification tool to categorize vehicles into two classes: cars and vans. The proposed technique was tested on a dataset of 60 vehicles comprising of a mix of frontal/rear and angular views. Experimental results prove that the proposed technique offers a very high level of accuracy thereby promising applicability in real-life situations.

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Quang A Nguyen, Martins E Irhebhude, Mohammad A Ali, Eran A Edirisinghe, "Vehicle type recognition using multiple-feature combinationsin Proc. IS&T Int’l. Symp. on Electronic Imaging: Video Surveillance and Transportation Imaging Applications,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.3.VSTIA-515

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