Abstract
In this paper, we propose a symbolic approach for classification of traffic video shots into light, medium, and heavy classes based on their content (congestion). We propose to represent a traffic video shot by an interval valued features. Unlike the conventional methods, the interval valued feature representation is able to preserve the variations existing among the extracted features of a traffic video shot. Based on the proposed symbolic representation, we present a symbolic method of classifying traffic video shots. The symbolic classification method makes use of a symbolic similarity measure for classification. An experimentation is carried out on a benchmark traffic video database. Experimental results reveal the efficacy of the proposed symbolic classification model. Moreover, it achieves classification within negligible time as it is based on a simple matching scheme.
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© 2013 Springer International Publishing Switzerland
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Dallalzadeh, E., Guru, D.S., Harish, B.S. (2013). Symbolic Classification of Traffic Video Shots. In: Nagamalai, D., Kumar, A., Annamalai, A. (eds) Advances in Computational Science, Engineering and Information Technology. Advances in Intelligent Systems and Computing, vol 225. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00951-3_2
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DOI: https://doi.org/10.1007/978-3-319-00951-3_2
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00950-6
Online ISBN: 978-3-319-00951-3
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