Symbolic Classification of Traffic Video Shots Elham Shakibapour en Guru,DS en Harish,BS en 2018-01-17T15:18:41Z 2018-01-17T15:18:41Z 2013 en
dc.description.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. en
dc.identifier.uri en
dc.language eng en
dc.relation 7034 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Symbolic Classification of Traffic Video Shots en
dc.type conferenceObject en
dc.type Publication en
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