This paper present an efficient real time rectangle speed limit sign recognition system. The system design considers computation load and hardware resources for driver assistant system. First multi-scale overlapping LBP features are used to train AdaBoost cascade classifier for speed limit sign object detection. Then a simple linear prediction method is used to do tracking task. At the recognition stage, a novel efficient algorithm is used to correct rotation angle, and then integral image based adaptive threshold algorithm is adopted to segment the speed limit number. The clustering based binary tree of linear support vector machine is adopted for classification task. The system is tested on real road scene video sequences. It achieves 98.3% recognition rate with approximate 16 fps frame rate on laptop.


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    Title :

    An efficient real time rectangle speed limit sign recognition system


    Contributors:


    Publication date :

    2010-06-01


    Size :

    1739548 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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