Surveillance drones equipped with video transmission capabilities play a crucial role in modern security systems, with the integration of OpenCV for object detection marking a significant advancement. This study evaluates the detecting accuracy by varying the distance between the drone and the target object, addressing gaps in current research through a detailed practical insight. This research explores key aspects of drone-based video surveillance, including object detecting, tracking and real-time following, aiming to enhance understanding and methodology in Computer Vision (CV) applications with Unmanned Aerial Vehicles (UAVs). The Simulation of a surveillance drone system in Gazebo simulator within the ROS2 framework. The drone is programmed to dynamically follow and track a mobile robot, enabling systematic analysis of the object detection algorithm's performance. The findings contribute to advancing the reliability and effectiveness of drone-based video surveillance systems for future innovations in security and monitoring applications.


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    Titel :

    Real-Time Drone System for Detecting, Tracking, and Following of a Mobile Robot in ROS/Gazebo


    Beteiligte:
    Salman, Ali (Autor:in) / Dika, Mohammad (Autor:in) / Diab, Hassan (Autor:in) / Owayjan, Michel (Autor:in) / Achkar, Roger (Autor:in)


    Erscheinungsdatum :

    11.02.2025


    Format / Umfang :

    548857 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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