Vision-based UAV tracking using deep reinforcement learning with simulated data

Mayssa Zaier; Wided Miled; Moulay A. Akhloufi

doi:10.1117/12.2619250

6 June 2022 Vision-based UAV tracking using deep reinforcement learning with simulated data

Mayssa Zaier, Wided Miled, Moulay A. Akhloufi

Proceedings Volume 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022; 121150C (2022) https://doi.org/10.1117/12.2619250
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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%.

Conference Presentation

Citation Download Citation

Mayssa Zaier, Wided Miled, and Moulay A. Akhloufi "Vision-based UAV tracking using deep reinforcement learning with simulated data", Proc. SPIE 12115, Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea and Space Vehicles and Infrastructure 2022, 121150C (6 June 2022); https://doi.org/10.1117/12.2619250

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