High-performance on-road vehicle detection with non-biased cascade classifier by weight-balanced training

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