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.
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