Abstract: To get the initialized background from the training sequence with foreground objects, a new background initialization algorithm was proposed based on clustering classifier. In this method, all stable sub-intervals in the training sequence were located for each pixel as possible background. Then a classify data set was constructed by the median values of each subinterval. A background subset was obtained from the data set by unsupervised clustering. Accordingly the initialized background was obtained from the background subset. By experiments, the proposed method was compared with the median method and the sample consensus method under different traffic conditions. Results show that this method is robust, overcomes the influence of slow moving big objects, and tolerates over 50% foreground pixels and noises.

Key words: computer application, video detection, background model, initialization background, traffic flow detection, unsupervised clustering