Now-a-days roads are monitored by CCTV cameras passively i.e. knowledge is not generated by the CCTV cameras. Human resource constraint i.e. there is very limited posting of the traffic police staff at road crossings and highway's. In many cases, accidents are reported due to negligence by the road users. Adding to this, even the majority of humans surrounding the accident spot/scene are busy clicking photos and videos unknown of the fact that their little negligence could cost a LIFE. The footage is only seen by the authorities only after a problem has been occurred so as to find the root cause of it. To overcome this, we propose an intelligent system that can make use of the existing CCTV cameras. The proposed system captures video stream, computes the input and the system alerts are generated in real time, which means no additional sensors would be required. U sing camera itself, the plan is to detect accidents in real time and also send alerts to ambulances or medical services; so that appropriate resources needed for saving lives are available in time. Other targets are: to detect vehicles which break the speed limit rules and relay this information to a concerned authority; to monitor restricted roads areas and traffic signal violations, and plan to detect number plates of the violators and find them. Along with this the proposed system will also classify the type of vehicles that run on road, so that further construction of roads can be tailored as per the expected vehicles for smoother traffic flow. In this study we will be taking help of algorithms such as Background Subtraction, Morphological transformations and many other basic concepts to perform the tasks mentioned above. By using technology at its best, the aim is to integrate various factors into one smart system. The project will benefit to reduce cost of road surveillance system and complete automation of road surveillance system.


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    Title :

    Smart Road Surveillance Using Image Processing


    Contributors:


    Publication date :

    2018-01-01


    Size :

    2857820 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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