Asymmetric totally-corrective boosting for real-time object detection

Authors:
Peng Wang;Chunhua Shen;Nick Barnes;Hong Zheng;Zhang Ren
Affiliations:
Beihang University, Beijing, China;NICTA, Canberra Research Laboratory, Canberra, ACT, Australia and Australian National University, Canberra, ACT, Australia;NICTA, Canberra Research Laboratory, Canberra, ACT, Australia and Australian National University, Canberra, ACT, Australia;Beihang University, Beijing, China;Beihang University, Beijing, China
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Year:
2010

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Abstract

Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learning problem. We show that our methods explicitly optimize asymmetric loss objectives in a totally corrective fashion. The methods are totally corrective in the sense that the coefficients of all selected weak classifiers are updated at each iteration. In contract, conventional boosting like AdaBoost is stagewise in that only the current weak classifier's coefficient is updated. At the heart of the totally corrective boosting is the column generation technique. Experiments on face detection show that our methods outperform the state-of-the-art asymmetric boosting methods.