Open AccessSimulation and Detection of Small Drones/Suspicious UAVs in Drone Grid
Author(s) -
Arpit Gupta
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36144
Subject(s) - drone , computer science , artificial intelligence , tracking (education) , computer vision , object detection , tracking system , robot , video tracking , grid , real time computing , object (grammar) , kalman filter , pattern recognition (psychology) , geography , psychology , pedagogy , genetics , geodesy , biology
Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and the tracking. The detection is based on the YOLOv3 (You Only Look Once v3) algorithm and a sliding window method. The tracking is based on the GOTURN (Generic Object Tracking Using Regression Networks) algorithm, which allows the tracking of generic objects at high speed. In order to allow autonomous tracking and enhance the accuracy, a combination of GOTURN and tracking by detection using YOLOv3 was developed.