Traffic light recognition is of great interest for advanced driver assistance systems and autonomous driving but still an unsolved problem. While a traffic light has few visual features for detection from camera images we believe the characteristic light represents a potentially very strong and stable feature. The traffic light is actively emitting light which is rarely influenced by weather or lighting condition. When using a color lookup table for an image segmentation-based object detector, the process of creating the lookup table is the crucial point. In this paper, we propose a method for generating a lookup table using real world data of a large dataset. The training data is sampled from labeled objects and stored as multisets. We contribute a frequency-based filtering method to clean the samples before using a k-nearest neighbor classifier to generalize. The result is stored as a three dimensional lookup table. The main contribution is a neighborhood-biasing technique that allows setting different operating points online without retraining. A challenging real world dataset containing several thousands of active lights is used to evaluate the process.


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    Title :

    Learning Traffic Light Colors


    Contributors:


    Publication date :

    2018-11-01


    Size :

    2690151 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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