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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Vehicle detection using discriminatively trained part templates with variable size


    Beteiligte:


    Erscheinungsdatum :

    2012-06-01


    Format / Umfang :

    1366487 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Vehicle Detection Using Discriminatively Trained Part Templates with Variable Size

    Tehrani Nik Nejad, H. / Kawano, T. / Mita, S. et al. | British Library Conference Proceedings | 2012




    Discriminatively Learned Hierarchical Rank Pooling Networks

    Fernando, B. / Gould, S. | British Library Online Contents | 2017


    Set2Model networks: Learning discriminatively to learn generative models

    Vakhitov, Alexander / Kuzmin, Andrey / Lempitsky, Victor | British Library Online Contents | 2018