Abstract We develop a method that can detect humans in a single image based on a new cascaded structure. In our approach, both the rectangle features and 1-D edge-orientation features are employed in the feature pool for weak-learner selection, which can be computed via the integral-image and the integral-histogram techniques, respectively. To make the weak learner more discriminative, Real AdaBoost is used for feature selection and learning the stage classifiers from the training images. Instead of the standard boosted cascade, a novel cascaded structure that exploits both the stage-wise classification information and the inter-stage cross-reference information is proposed. Experimental results show that our approach can detect people with both efficiency and accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection


    Contributors:


    Publication date :

    2007-01-01


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Pedestrian Detection and Direction Estimation by Cascade Detector with Multi-Classifiers Utilizing Feature Interaction Descriptor

    Goto, K. / Kidono, K. / Yoshikatsu, K. et al. | British Library Conference Proceedings | 2011


    Pedestrian detection by scene dependent classifiers with generative learning

    Yoshida, Hidefumi / Suzuo, Daichi / Deguchi, Daisuke et al. | IEEE | 2013


    Pedestrian detection in traffic scenes using multi-attitude classifiers

    Brehar, Raluca / Nedevschi, Sergiu | IEEE | 2013



    PEDESTRIAN DETECTION BY SCENE DEPENDENT CLASSIFIERS WITH GENERATIVE LEARNING

    Yoshida, H. / Suzuo, D. / Deguchi, D. et al. | British Library Conference Proceedings | 2013