Improving Detector of Viola and Jones through SVM


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.

Computer Vision – ACCV 2010 Workshops
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.