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Conference Paper

How Good are Detection Proposals, really?

MPS-Authors
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Hosang,  Jan
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Benenson,  Rodrigo
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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paper082.pdf
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sup082.zip
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Citation

Hosang, J., Benenson, R., & Schiele, B. (2014). How Good are Detection Proposals, really? In M. Valstar, A. French, & T. Pridmore (Eds.), Proceedings of the British Machine Vision Conference (pp. 1-12). Durham: BMVA Press.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0024-3C2E-2
Abstract
Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images. Despite the popularity of detection proposals, it is unclear which trade‐offs are made when using them during object detection. We provide an in depth analysis of ten object proposal methods along with four baselines regarding ground truth annotation recall (on Pascal VOC 2007 and ImageNet 2013), repeatability, and impact on DPM detector performance. Our findings show common weaknesses of existing methods, and provide insights to choose the most adequate method for different settings.