In recent years, Unmanned Aerial Vehicles (UAVs) have seen significant technological advances, with a wide range of applications. However, their arbitrary uses continue to pose a great threat to public safety and privacy. This has sparked the interest of the research community, which is developing solutions based on Artificial Intelligence (AI) to detect and track in real time these unmanned flying objects in sensitive areas. In this paper, we propose a vision-based Deep Reinforcement Learning (DRL) algorithm to track drones in various simulated scenarios, within the Microsoft AirSim simulator. The proposed approach is promising and achieves high tracking accuracy in different realistic simulated environments. It allowed to process videos at high frame rates and achieved a mean average precision (mAP) above 80%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.