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Aerospace 2017, 4(2), 32; doi:10.3390/aerospace4020032

Aerial Target Tracking Algorithm Based on Faster R-CNN Combined with Frame Differencing

College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Road, Nanjing 210016, China
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Academic Editor: Michael Wing
Received: 24 April 2017 / Revised: 25 May 2017 / Accepted: 12 June 2017 / Published: 20 June 2017
(This article belongs to the Collection Unmanned Aerial Systems)
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Abstract

We propose a robust approach to detecting and tracking moving objects for a naval unmanned aircraft system (UAS) landing on an aircraft carrier. The frame difference algorithm follows a simple principle to achieve real-time tracking, whereas Faster Region-Convolutional Neural Network (R-CNN) performs highly precise detection and tracking characteristics. We thus combine Faster R-CNN with the frame difference method, which is demonstrated to exhibit robust and real-time detection and tracking performance. In our UAS landing experiments, two cameras placed on both sides of the runway are used to capture the moving UAS. When the UAS is captured, the joint algorithm uses frame difference to detect the moving target (UAS). As soon as the Faster R-CNN algorithm accurately detects the UAS, the detection priority is given to Faster R-CNN. In this manner, we also perform motion segmentation and object detection in the presence of changes in the environment, such as illumination variation or “walking persons”. By combining the 2 algorithms we can accurately detect and track objects with a tracking accuracy rate of up to 99% and a frame per second of up to 40 Hz. Thus, a solid foundation is laid for subsequent landing guidance. View Full-Text
Keywords: deep learning; Faster R-CNN; UAS landing; object detection deep learning; Faster R-CNN; UAS landing; object detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yang, Y.; Gong, H.; Wang, X.; Sun, P. Aerial Target Tracking Algorithm Based on Faster R-CNN Combined with Frame Differencing. Aerospace 2017, 4, 32.

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