Traffic congestion classification using motion vector statistical features

Amina Riaz; Shoab A. Khan

doi:10.1117/12.2051463

24 December 2013 Traffic congestion classification using motion vector statistical features

Amina Riaz, Shoab A. Khan

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90671A (2013) https://doi.org/10.1117/12.2051463
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

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.

Citation Download Citation

Amina Riaz and Shoab A. Khan "Traffic congestion classification using motion vector statistical features", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90671A (24 December 2013); https://doi.org/10.1117/12.2051463

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