Pedestrian detection systems are receiving increasing attention in both industry and academia with the rapid development of autonomous automobiles which employ artificial intelligence. These systems must detect specific classes of objects such as pedestrians rather than generic objects. In this paper, we present a faster RCNN based pedestrian detection system which improves upon previous solutions. The proposed model takes arbitrary size images as inputs and generates bounding boxes and confidence scores for pedestrians. The system achieves good performance and is faster than the well known and frequently used methods in the literature.


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    Titel :

    A Faster RCNN-Based Pedestrian Detection System


    Beteiligte:
    Zhao, Xiaotong (Autor:in) / Li, Wei (Autor:in) / Zhang, Yifang (Autor:in) / Gulliver, T. Aaron (Autor:in) / Chang, Shuo (Autor:in) / Feng, Zhiyong (Autor:in)


    Erscheinungsdatum :

    2016-09-01


    Format / Umfang :

    998786 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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