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

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.

Subject Areas :
Views 15
Downloads 1
 articleview.views 15
 articleview.downloads 1
  Cite this article 

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,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2016
Electronic Imaging
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA