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

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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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF96-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-81D6-A
Genre: Conference Paper

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 Creators:
Lampert, CH1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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.

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 Dates: 2010-06
 Publication Status: Published in print
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 Identifiers: DOI: 10.1109/CVPR.2010.5540107
BibTex Citekey: 6770
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Title: Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010)
Place of Event: San Francisco, CA, USA
Start-/End Date: 2010-06-13 - 2010-06-18

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Title: Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010)
Source Genre: Proceedings
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Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1022 - 1029 Identifier: ISBN: 978-1-424-47029-7