期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 15, 期 2, 页码 168-172出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2776899
关键词
Data fusion; kernel correlation filter (KCF); object tracking; satellite video; three-frame difference
类别
资金
- National Natural Science Foundation of China [61601333, 61471274]
- China Postdoctoral Science Foundation [2016T90733]
- Natural Science Foundation of Hubei Province of China [2016CFB245]
- Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences [2015LDE001]
Object tracking is a popular topic in the field of computer vision. The detailed spatial information provided by a very high resolution remote sensing sensor makes it possible to track targets of interest in satellite videos. In recent years, correlation filters have yielded promising results. However, in terms of dealing with object tracking in satellite videos, the kernel correlation filter (KCF) tracker achieves poor results due to the fact that the size of each target is too small compared with the entire image, and the target and the background are very similar. Therefore, in this letter, we propose a new object tracking method for satellite videos by fusing the KCF tracker and a three-frame-difference algorithm. A specific strategy is proposed herein for taking advantage of the KCF tracker and the three-frame-difference algorithm to build a strong tracker. We evaluate the proposed method in three satellite videos and show its superiority to other state-of-the-art tracking methods.
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