Local Context Priors for Object Proposal Generation

  • Marko Ristin
  • Juergen Gall
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)


State-of-the-art methods for object detection are mostly based on an expensive exhaustive search over the image at different scales. In order to reduce the computational time, one can perform a selective search to obtain a small subset of relevant object hypotheses that need to be evaluated by the detector. For that purpose, we employ a regression to predict possible object scales and locations by exploiting the local context of an image. Furthermore, we show how a priori information, if available, can be integrated to improve the prediction. The experimental results on three datasets including the Caltech pedestrian and PASCAL VOC dataset show that our method achieves the detection performance of an exhaustive search approach with much less computational load. Since we model the prior distribution over the proposals locally, it generalizes well and can be successfully applied across datasets.


Patch Size Object Detection Global Prior Local Prior Pedestrian Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marko Ristin
    • 1
  • Juergen Gall
    • 2
  • Luc Van Gool
    • 1
    • 3
  1. 1.ETH ZurichSwitzerland
  2. 2.MPI for Intelligent SystemsGermany
  3. 3.KU LeuvenBelgium

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