Highway Traffic Congestion Classification using Holistic Properties

Andrews Sobral, Luciano Oliveira, Leizer Schnitman, and Felippe De Souza


Pattern Recognition, Object Recognition and Motion, Neural Network Applications


This work proposes a holistic method for highway traffic video classification based on vehicle crowd properties. The method classifies the traffic congestion into three classes: light, medium and heavy. This is done by usage of average crowd density and crowd speed. Firstly, the crowd density is estimated by background subtraction and the crowd speed is performed by pyramidal Kanade-Lucas-Tomasi (KLT) tracker algorithm. The features classification with neural networks show 94.50% of accuracy on experimental results from 254 highway traffic videos of UCSD data set.

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