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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Local Context Priors for Object Proposal Generation
verfasst von : Marko Ristin, Juergen Gall, Luc Van Gool
Erschienen in: Computer Vision – ACCV 2012
Verlag: Springer Berlin Heidelberg
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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.