In this paper, we present a learning-based brake light classification algorithm for intelligent driver-assistance systems. State-of-the-art approaches apply different image processing techniques with hand-crafted features to determine whether brake lights are on or off. In contrast, we learn a brake light classifier based on discriminative color descriptors and convolutional features fine-tuned for traffic scenes. We show how brake light regions can be segmented and classified in one framework. Numerous experimental results show that the proposed algorithm performs well against state-of-the-art alternatives in real-world scenes.


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    Titel :

    Learning to tell brake lights with convolutional features


    Beteiligte:
    Guangyu Zhong (Autor:in) / Yi-Hsuan Tsai (Autor:in) / Yi-Ting Chen (Autor:in) / Xue Mei (Autor:in) / Prokhorov, Danil (Autor:in) / James, Michael (Autor:in) / Ming-Hsuan Yang (Autor:in)


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    710385 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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