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Journal of Electronic Imaging

Automatic vehicle classification using linked visual words
Author(s): Nattachai Watcharapinchai; Supavadee Aramvith; Supakorn Siddhichai
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Paper Abstract

An improvement in the method of automatic vehicle classification is investigated. The challenges are to correctly classify vehicles regardless of changes in illumination, differences in points of view of the camera, and variations in the types of vehicles. Our proposed appearance-based feature extraction algorithm is called linked visual words (LVWs) and is based on the existing technique bag-of-visual word (BoVW) with the addition of spatial information to improve accuracy of classification. In addition, to prevent over-fitting due to a large number of LVWs, four common sampling techniques with LVWs are investigated. Our results suggest that the sampling of LVWs using TF-IDF with grouping improved the accuracy of classification for the test dataset. In summary, the proposed system is able to classify nine types of vehicles and work with surveillance cameras in real-world scenarios. The classification accuracy of the proposed system is 5.58% and 4.27% higher on average for three datasets when compared with BoVW + SVM and Lenet-5, respectively.

Paper Details

Date Published: 25 July 2017
PDF: 14 pages
J. Electron. Imag. 26(4) 043009 doi: 10.1117/1.JEI.26.4.043009
Published in: Journal of Electronic Imaging Volume 26, Issue 4
Show Author Affiliations
Nattachai Watcharapinchai, Chulalongkorn Univ. (Thailand)
Supavadee Aramvith, Chulalongkorn Univ. (Thailand)
Supakorn Siddhichai, National Electronics and Computer Technology Ctr. (Thailand)

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