Vehicle Detection And Recognition Based On Edge Contour Enhance Gaussian Kalman Model And Bayesian Decision Tree

In the ITS field, moving object detection and vehicle identification problem hasalways been the research focus and priorities. Because of its characteristics andadvantages, image processing and pattern recognition is widely applied to the field ofintelligent transportation. In this paper, the Yangtze River Bridge security monitoringproject to explore in-depth study of traffic scene detect moving targets and identify thetopics of the target category, based on image analysis and pattern recognition methods, themain contents include moving vehicle detection, vehicle identification.Firstly, the moving target detection.target detection background subtraction methodusing a simplified Gaussian model are discussed. Insufficient for simplified Gaussianmodel, the problem of how to detect the movement of vehicles with a similar background,this paper, using simplified Gaussian model and moving object edge contours to determineit with a similar background, to recover its possible lack of contour region algorithm.Experimental results show that, This paper presents a moving vehicle detection methodcan detect the moving targets with a similar background well.The Bayesian algorithm specific application to the preliminary identify areas ofclassification, the preliminary identification of models based on Bayesian methods anddecision trees. Appropriate models feature by selecting the size, shape, edge characteristics,training samples and strike each feature attribute in Bayesian parameter in theclassification of the various types of models that mean-variance, and then according to thedecision tree Bayesian classifier do vehicle Recognition. According to the experimentalresults, the decision tree enhanced Bayesian classifier effect better.