In this paper, a general algorithm for pedestrian detection by on-board monocular camera which can be applied to cameras of various view ranges in unified manner. The Spatio-Temporal MRF model extracts and tracks foreground objects as pedestrians and non-pedestrian distinguishing from background scenes as buildings by referring to motion difference. During the tracking sequences, cascaded HOG classifiers classify the foreground objects into the two classes of pedestrians and non-pedestrians. Before the classification, geometrical constraints on the relationship between heights and positions of the objects are examined to exclude the non-pedestrian objects. This pre-processing contributed to reducing the processing time of the classification while maintaining the classification accuracy. Due to the benefit of the tracking that the classifier can make decision totally considering Regions of Interest (ROIs) with same ID during consecutive images, this algorithm can operates quite robustly against noises and classification errors at each image frame.


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    Title :

    Pedestrian detection algorithm for on-board cameras of multi view angles


    Contributors:
    Kamijo, S (author) / Fujimura, K (author) / Shibayama, Y (author)


    Publication date :

    2010-06-01


    Size :

    2066290 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pedestrian Detection Algorithm for On-Board Cameras of Multi View Angles, pp. 973-980

    Kamijo, S. / Fujimura, K. / Shibayama, Y. et al. | British Library Conference Proceedings | 2010


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