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Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications.

Li-Yu Lo1, Chi Hao Yiu1, Yu Tang1

  • 1Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.

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Summary
This summary is machine-generated.

This study presents an autonomous unmanned aerial vehicle (UAV) system for target detection and tracking. The system uses YOLOv4-Tiny and Kalman filters for robust surveillance, enhancing autonomous capabilities.

Keywords:
Kalman FilterUAVautonomous surveillancedeep learningobject detectionobject tracking

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous unmanned aerial vehicles (UAVs) are increasingly used in real-world applications.
  • UAVs with vision systems offer potential for surveillance tasks.
  • Current autonomous surveillance systems require further development for enhanced performance.

Purpose of the Study:

  • To propose a learning-based UAV system for autonomous surveillance.
  • To enable UAVs to autonomously detect, track, and follow target objects without human intervention.
  • To integrate perception and path planning for a fully autonomous surveillance system.

Main Methods:

  • Utilized the YOLOv4-Tiny algorithm for semantic object detection.
  • Integrated 3D object pose estimation and a Kalman filter to enhance perception.
  • Implemented UAV path planning for surveillance maneuvers.

Main Results:

  • The perception module was assessed on a quadrotor UAV.
  • The complete autonomous system was validated through flight experiments.
  • Experimental results confirmed the system's robustness, effectiveness, and reliability in surveillance tasks.

Conclusions:

  • The developed learning-based UAV system effectively performs autonomous object detection, tracking, and following.
  • The integrated approach enhances perception and enables complex surveillance maneuvers.
  • The system demonstrates robustness and reliability for real-world surveillance applications.