Pedestrian detection is a challenging task due to the high variance of pedestrians and fast changing background, especially for a single in-car camera system. Traditional HOG+SVM methods have two challenges: (1) false positives and (2) processing speed. In this paper, a new pedestrian detection method using multimodal HOG for pedestrian feature extraction and kernel based Extreme Learning Machine (ELM) for classification is presented. The experimental results using our naturalistic driving dataset show that the proposed method outperforms the traditional HOG+SVM method in both recognition accuracy and processing speed.


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    Titel :

    An Extreme Learning Machine-based pedestrian detection method


    Beteiligte:
    Yang, Kai (Autor:in) / Du, Eliza Y. (Autor:in) / Delp, Edward J. (Autor:in) / Jiang, Pingge (Autor:in) / Jiang, Feng (Autor:in) / Chen, Yaobin (Autor:in) / Sherony, Rini (Autor:in) / Takahashi, Hiroyuki (Autor:in)


    Erscheinungsdatum :

    01.06.2013


    Format / Umfang :

    1012694 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Machine Learning Based Pedestrian Detection and Tracking for Autonomous Vehicles

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    PEDESTRIAN DETECTION DEVICE, PEDESTRIAN DETECTION SYSTEM, AND PEDESTRIAN DETECTION METHOD

    TANIGUCHI SUGURU | Europäisches Patentamt | 2018

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