Content-based classification of traffic videos using symbolic features

Dallalzadeh, E. and Guru, D. S. (2014) Content-based classification of traffic videos using symbolic features. In: Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014.

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Official URL: https://doi.org/10.1109/ICACCI.2014.6968213

Abstract

In this paper, we propose a symbolic approach for classification of traffic videos based on their content. We propose to represent a traffic video 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. Based on the proposed symbolic representation, we present a method of classifying traffic videos. The proposed classification method makes use of symbolic similarity computation and dissimilarity computation to classify the traffic videos into light, medium, and heavy traffic congestion. An experimentation is carried out on a benchmark traffic video database. Experimental results reveal the ability of the proposed model for classification of traffic videos based on their content.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Interval-valued, Symbolic features, Symbolic similarity, Computer programming, Dissimilarity measures, Traffic congestion, Traffic videos
Subjects: D Physical Science > Computer Science
Divisions: Department of > Computer Science
Depositing User: Arshiya Kousar
Date Deposited: 17 Jun 2019 04:20
Last Modified: 17 Jun 2019 04:20
URI: http://eprints.uni-mysore.ac.in/id/eprint/3188

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