Object Tracking in Satellite Videos by Improved Correlation Filters With Motion Estimations

As a new method of Earth observation, video satellite is capable of monitoring specific events on the Earth's surface continuously by providing high-temporal resolution remote sensing images. The video observations enable a variety of new satellite applications such as object tracking and road...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2020-02, Vol.58 (2), p.1074-1086
Main Authors: Xuan, Shiyu, Li, Shengyang, Han, Mingfei, Wan, Xue, Xia, Gui-Song
Format: Article
Language:English
Subjects:
Algorithms
Correlation
Correlation filter
Earth
Earth observations (from space)
Earth surface
Filters
Kalman filters
Monitoring
motion estimation (ME)
Motion simulation
Movement
Moving object recognition
Object tracking
Optical filters
Remote sensing
Satellite observation
Satellite tracking
satellite videos
Satellites
Temporal resolution
Videos
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Summary:As a new method of Earth observation, video satellite is capable of monitoring specific events on the Earth's surface continuously by providing high-temporal resolution remote sensing images. The video observations enable a variety of new satellite applications such as object tracking and road traffic monitoring. In this article, we address the problem of fast object tracking in satellite videos, by developing a novel tracking algorithm based on correlation filters embedded with motion estimations. Based on the kernelized correlation filter (KCF), the proposed algorithm provides the following improvements: 1) proposing a novel motion estimation (ME) algorithm by combining the Kalman filter and motion trajectory averaging and mitigating the boundary effects of KCF by using this ME algorithm and 2) solving the problem of tracking failure when a moving object is partially or completely occluded. The experimental results demonstrate that our algorithm can track the moving object in satellite videos with 95% accuracy.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2019.2943366