Fast car/human classification methods in the computer vision tasks

In this paper we propose a method for classification of moving objects of “human” and “car” types in computer vision systems using statistical hypotheses and integration of the results using two different decision rules. FAR-FRR graphs for all criteria and the decision rule are plotted. Confusion matrix for both ways of integration is presented. The example of the method application to the public video databases is provided. Ways of accuracy improvement are proposed.