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.
Real-Time Drone System for Detecting, Tracking, and Following of a Mobile Robot in ROS/Gazebo
2025-02-11
548857 byte
Conference paper
Electronic Resource
English
ROS2-Gazebo Simulator for Drone Applications
IEEE | 2024
|Drone Stability Simulation Using ROS and Gazebo
Springer Verlag | 2021
|Simulation of a Differential Robot Drive in Gazebo
IEEE | 2023
|