The rising number of traffic rule violators, as well as the resulting mishaps,
    has made road safety a major concern. Detection of traffic rule violators is challenging
    due to various difficulties such as occlusion, illumination, poor quality of surveillance
    video, varying weather conditions, etc. The existing surveillance systems are primarily
    dependent on the performance of human operators who are unrealistically expected to
    simultaneously watch many screens that show streams captured by different cameras.
    The task of these operators is becoming more difficult as the number of simultaneous
    video streams to watch increases. It is well-known that after twenty minutes of
    continuous work, the operator's attention degrades significantly. Thus, there is a need
    for an automatic detection system. This research aims to increase the installations of
    new cameras covering more areas. It will help overcome all the difficulties mentioned
    above and ensure motorcyclists' detection without helmets using the CCTV cameras'
    footage and machine learning algorithms. It will help law enforcement by police and
    eventually change the risk behaviors of motorcyclists. Consequently, the number of
    accidents and their severity will reduce.


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    Title :

    Helmet Violation Processing for Law Enforcement using CCTV Camera




    Publication date :

    2022



    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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