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Paper

What Makes for Effective Detection Proposals?

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons86681

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons79212

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

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

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Fulltext (public)

1502.05082.pdf
(Preprint), 5MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Hosang, J., Benenson, R., Dollár, P., & Schiele, B. (2015). What Makes for Effective Detection Proposals? Retrieved from http://arxiv.org/abs/1502.05082.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-CCFC-6
Abstract
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL and ImageNet, and impact on DPM and R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detector performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods.