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Proceedings Paper

Traffic congestion classification using motion vector statistical features
Author(s): Amina Riaz; Shoab A. Khan
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Paper Abstract

Due to the rapid increase in population, one of the major problems faced by the urban areas is traffic congestion. In this paper we propose a method for classifying highway traffic congestion using motion vector statistical properties. Motion vectors are estimated using pyramidal Kanada-Lucas-Tomasi (KLT) tracker algorithm. Then motion vector features are extracted and are used to classify the traffic patterns into three categories: light, medium and heavy. Classification using neural network, on publicly available dataset, shows an accuracy of 95.28%, with robustness to environmental conditions such as variable luminance. Our system provides a more accurate solution to the problem as compared to the systems previously proposed.

Paper Details

Date Published: 24 December 2013
PDF: 7 pages
Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671A (24 December 2013); doi: 10.1117/12.2051463
Show Author Affiliations
Amina Riaz, College of E&ME, NUST (Pakistan)
Shoab A. Khan, College of E&ME, NUST (Pakistan)

Published in SPIE Proceedings Vol. 9067:
Sixth International Conference on Machine Vision (ICMV 2013)
Branislav Vuksanovic; Antanas Verikas; Jianhong Zhou, Editor(s)

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