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Vehicle detection using discriminatively trained part templates with variable size | IEEE Conference Publication | IEEE Xplore

Vehicle detection using discriminatively trained part templates with variable size

Publisher: IEEE

Abstract:

Introduction of new local and semi-local features has played an important role in advancing the performance of object recognitions. Deformable part models prepare elegant...View more

Abstract:

Introduction of new local and semi-local features has played an important role in advancing the performance of object recognitions. Deformable part models prepare elegant framework for representing object categories and both efficient and accurate, achieving state-of the-art results. In this paper, We consider the problem of training a part-based model with variable size from images labeled only with bounding boxes around the objects. We consider part size as a latent variable and try to optimize simultaneously size and place of part templates to cover high-energy regions of the object. Extensive experiments in urban scenarios for vehicle detection show that the average precision of deformable part model significantly is improved from 72.10% to 82.41% without losing the average recall.
Date of Conference: 03-07 June 2012
Date Added to IEEE Xplore: 05 July 2012
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: Madrid, Spain

References

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