Abstract
This paper describes a method to classify vehicle type using computer vision technology. In this study, Visual Background Extractor (ViBe) was used to extract the vehicles from the captured videos. The features of the detected vehicles were extracted using Histogram of Oriented Gradient (HOG). Multi-class Support Vector Machine (SVM) was used to recognise four classes of images: motorcycle, car, lorry and background (without vehicles). The results show that the proposed classifier was able to achieve an average accuracy of 92.3 %.
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Acknowledgments
Specially thanks to Chih-Chung Chang and Chih-Jen Lin for sharing LIBSVM, which contributed greatly to this study.
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© 2014 Springer Science+Business Media Singapore
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Ng, L.T., Suandi, S.A., Teoh, S.S. (2014). Vehicle Classification Using Visual Background Extractor and Multi-class Support Vector Machines. In: Mat Sakim, H., Mustaffa, M. (eds) The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications. Lecture Notes in Electrical Engineering, vol 291. Springer, Singapore. https://doi.org/10.1007/978-981-4585-42-2_26
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DOI: https://doi.org/10.1007/978-981-4585-42-2_26
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