Nowadays, fish-eye cameras play an increasingly important role in intelligent vehicles because of its wide field of view. Using fish-eye camera, pedestrians around the vehicles could be monitored expediently, but the problem of pedestrian distortion has always existed. This paper creates a new warping pedestrian benchmark using imaging principle of the fish-eye camera based on ETH pedestrian benchmark. With this practical benchmark, warping pedestrians are trained differently according to the position in fish-eye images. A self-adapting part-based algorithm is proposed to detect pedestrian with different degrees of deformation. Moreover, GPU is used to accelerate the whole algorithm to guarantee the real-time performance. Experiments show that the algorithm has competitive accuracy.


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    Titel :

    Self-adapting part-based pedestrian detection using a fish-eye camera


    Beteiligte:
    Qian, Yeqiang (Autor:in) / Yang, Ming (Autor:in) / Wang, Chunxiang (Autor:in) / Wang, Bing (Autor:in)


    Erscheinungsdatum :

    2017-06-01


    Format / Umfang :

    1710681 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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