In low-or middle-income countries, road traffic safety becomes challenging because of the increasing number of vehicles, limited road infrastructure, and inability to capture a variety of traffic violations. Traffic Management and Information Control Centres (TMICCs) collect and analyse traffic data to improve road traffic safety. State-of-the-art technologies, such as the Internet of drones and computer vision, could help to automatically extract traffic data and provide a hands-free solution for road traffic monitoring. Hence, we propose a “UAV-based Urban Traffic Monitoring (U-UTM)” system to automatically extract traffic violations based on the movement of vehicles and traffic flow parameters, which helps to improve road traffic safety. Along with proposing a framework, this paper implemented three U-UTM operations - estimating Ground Sample Distance (GSD), detecting lanes using vehicles' tracks, and post-processing to reduce broken tracks. This paper derived GSD mapping from empirical data, improved it by 25.09% over theoretical GSD mapping, accurately detected lanes/zones, and reduced broken tracks of vehicles by 48.19 %. Videos, data, and codes are available at https://github.com/ERYAGNIK0031U-UTM.


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    Titel :

    U-UTM: A Cyber-Physical System for Road Traffic Monitoring Using UAVs


    Beteiligte:


    Erscheinungsdatum :

    17.12.2024


    Format / Umfang :

    17401322 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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