Many traffic accidents occurred at intersections are caused by drivers who miss or ignore the traffic signals. In this paper, we present a new method for automatic detection of traffic lights that integrates both image processing and support vector machine techniques. An experimental dataset with 21299 samples is built from the captured original videos while driving on the streets. When compared to the traditional object detection and existing methods, the proposed system provides significantly better performance with 96.97% precision and 99.43% recall. The system framework is extensible that users can introduce additional parameters to further improve the detection performance.


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    Title :

    Automatic detection of traffic lights using support vector machine


    Contributors:
    Chen, Zhilu (author) / Shi, Quan (author) / Huang, Xinming (author)


    Publication date :

    2015-06-01


    Size :

    269607 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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