In this paper, we propose a cascade classifier for high-performance on-road vehicle detection. The proposed system\ndeliberately selects constituent weak classifiers that are expected to show good performance in real detection\nenvironments. The weak classifiers selected at a cascade stage using AdaBoost are assessed for their effectiveness in\nvehicle detection. By applying the selected weak classifiers with their own confidence levels to another set of image\nsamples, the system observes the resultant weights of those samples to assess the biasing of the selected weak\nclassifiers. Once they are estimated as biased toward either positive or negative samples, the weak classifiers are\ndiscarded, and the selection process is restarted after adjusting the weights of the training samples. Experimental\nresults show that a cascade classifier using weak classifiers selected by the proposed method has a higher\ndetection performance.
Loading....