Bayesian Network Based Computer Vision Algorithm for Vehicle Classification from Incomplete Data

This paper presents algorithms for vision-based classification of vehicles in image sequences of traffic scenes recorded by a stationary camera. Here using a Bayesian network to classify objects into different types of vehicles, especially from incomplete data, that is, in the presence of missing values or hidden variables. Vehicles are modeled as rectangles patches with certain dynamic behavior which represented by features such as position, velocity etc in Bayesian network. The highly accurate classifications are very useful parameters in traffic monitoring systems. Experimental results from highway scenes are provided which demonstrate the effectiveness and robust of the method.