A Steiner tree approach to efficient object detection

Olga Russakovsky, Andrew Y. Ng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

We propose an approach to speeding up object detection, with an emphasis on settings where multiple object classes are being detected. Our method uses a segmentation algorithm to select a small number of image regions on which to run a classifier. Compared to the classical sliding window approach, this results in a significantly smaller number of rectangles examined, and thus significantly faster object detection. Further, in the multiple object class setting, we show that the computational cost of proposing candidate regions can be amortized across objects classes, resulting in an additional speedup. At the heart of our approach is a reduction to a directed Steiner tree optimization problem, which we solve approximately in order to select the segmentation algorithm parameters. The solution gives a small set of segmentation strategies that can be shared across object classes. Compared to the sliding window approach, our method results in two orders of magnitude fewer regions considered, and significant (10-15x) running time speedups on challenging object detection datasets (LabelMe and StreetScenes) while maintaining comparable detection accuracy.

Original languageEnglish (US)
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
Pages1070-1077
Number of pages8
DOIs
StatePublished - Aug 31 2010
Externally publishedYes
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 - San Francisco, CA, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Other

Other2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010
CountryUnited States
CitySan Francisco, CA
Period6/13/106/18/10

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Russakovsky, O., & Ng, A. Y. (2010). A Steiner tree approach to efficient object detection. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2010 (pp. 1070-1077). [5540097] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2010.5540097