25 July 2017 Automatic vehicle classification using linked visual words
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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.
© 2017 SPIE and IS&T
Nattachai Watcharapinchai, Nattachai Watcharapinchai, Supavadee Aramvith, Supavadee Aramvith, Supakorn Siddhichai, Supakorn Siddhichai, } "Automatic vehicle classification using linked visual words," Journal of Electronic Imaging 26(4), 043009 (25 July 2017). https://doi.org/10.1117/1.JEI.26.4.043009 . Submission: Received: 21 February 2017; Accepted: 29 June 2017
Received: 21 February 2017; Accepted: 29 June 2017; Published: 25 July 2017

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