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.
Vehicle detection using discriminatively trained part templates with variable size
2012 IEEE Intelligent Vehicles Symposium ; 766-771
2012-06-01
1366487 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vehicle Detection Using Discriminatively Trained Part Templates with Variable Size
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