This paper proposes a Network Shaped Cascade Classifier(NSCC) based on potential functions for pedestrian detection. Potential function is exploited to capture the nonlinear information in the training set based on the multiple sample centers. A flexible structure in NSCC is used to combine the base classifier and potential function into a nonlinear cascade classifier, and NSCC can well inherit the advantages of the base classifier. We test our classifier on INRIA dataset, and achieve a much better performance than support vector machine.


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    Title :

    A network shaped cascade classifier based on potential functions for pedestrian detection


    Contributors:


    Publication date :

    2014-08-01


    Size :

    231380 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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