Improving Detector of Viola and Jones through SVM

Abstract

Boosted cascade proposed by Viola and Jones is applied to many object detection problems. In their cascade, the confidence value of each stage can only be used in the current stage so that interstage information is not utilized to enhance classification performance. In this paper, we present a new cascading structure added SVM stages which employ the confidence values of multiple preceding Adaboost stages as input. Specifically, a rejection hyperplane and a promotion hyperplane are learned for each added SVM stage. During detection process, negative detection windows are discarded earier by the rejection SVM hyperplane, and positive windows with high confidence value are boosted by promotion hyperplane to bypass the next stage of cascade. In order to construct the two distinct hyperplanes, different cost coefficients for training samples are chosen in SVM learning. Experiment results in UIUC data set demonstrate that the proposed method achieve high detection accuracy and better efficiency.