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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    Title :

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


    Contributors:
    Qian, Yeqiang (author) / Yang, Ming (author) / Wang, Chunxiang (author) / Wang, Bing (author)


    Publication date :

    2017-06-01


    Size :

    1710681 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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