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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    Title :

    Learning to tell brake lights with convolutional features


    Contributors:


    Publication date :

    2016-11-01


    Size :

    710385 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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