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 den- sity 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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