Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV sys...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 21; no. 23; p. 7888
Main Authors: Lo, Li-Yu, Yiu, Chi Hao, Tang, Yu, Yang, An-Shik, Li, Boyang, Wen, Chih-Yung
Format: Journal Article
Language:English
Published: Basel MDPI AG 27.11.2021
MDPI
Subjects:
Algorithms
autonomous surveillance
Cameras
Deep learning
Embedded systems
Kalman Filter
Localization
object detection
object tracking
Sensors
Surveillance
UAV
Unmanned aerial vehicles
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21237888