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Conference Paper

An efficient divide-and-conquer cascade for nonlinear object detection


Lampert,  CH
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society;

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Lampert, C. (2010). An efficient divide-and-conquer cascade for nonlinear object detection. In Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010) (pp. 1022-1029). Piscataway, NJ, USA: IEEE.

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We introduce a method to accelerate the evaluation of object detection cascades with the help of a divide-and-conquer procedure in the space of candidate regions. Compared to the exhaustive procedure that thus far is the state-of-the-art for cascade evaluation, the proposed method requires fewer evaluations of the classifier functions, thereby speeding up the search. Furthermore, we show how the recently developed efficient subwindow search (ESS) procedure [11] can be integrated into the last stage of our method. This allows us to use our method to act not only as a faster procedure for cascade evaluation, but also as a tool to perform efficient branch-and-bound object detection with nonlinear quality functions, in particular kernelized support vector machines. Experiments on the PASCAL VOC 2006 dataset show an acceleration of more than 50 by our method compared to standard cascade evaluation.