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Simulation Study on Civil Aviation Human Reliability Learning from Incidents Using System Dynamics Read chapter Read first chapter

2023 | OriginalPaper | Chapter

A Unified Framework for Joint Moving Object Detection and Tracking in the Sky and Underwater

Authors : Xia Wu, Han Pan, Meng Xu, Zhongliang Jing, Min Bao

Published in: Proceedings of the International Conference on Aerospace System Science and Engineering 2021

Publisher: Springer Nature Singapore

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Abstract

The ability to detect and locate the moving object in a video is a fundamental procedure in applications of computer vision. However, these tracking methods still face some challenges, and are contradictory among different tasks. In this paper, a unified framework for joint moving object detection and tracking in the sky and underwater is proposed. This framework meets the requirements of two real applications: (i) tracking unmanned aerial vehicle (UAV) in the sky; and (ii) tracking unmanned underwater vehicle (UUV) in water. It consists of three key steps: (i) moving object detection by pixel classification; (ii) data association by blob detection; and (iii) object tracking by efficient convolution operator. Finally, analysis on the accuracy of the proposed framework is provided. Experimental results on real-world datasets and object tracking benchmark (OTB) demonstrate the advantage of the tracking method compared with some state-of-the-art trackers, in terms of accuracy and robustness. In addition, to the best of the authors’ knowledge, there is no previously published work for joint moving target detection and tracking in the sky and underwater.

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Metadata
Title
A Unified Framework for Joint Moving Object Detection and Tracking in the Sky and Underwater
Authors
Xia Wu
Han Pan
Meng Xu
Zhongliang Jing
Min Bao
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of the International Conference on Aerospace System Science and Engineering 2021

Print ISBN: 978-981-16-8153-0

Electronic ISBN: 978-981-16-8154-7

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-16-8154-7_17

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