期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 11, 页码 8719-8731出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2922648
关键词
Target tracking; Satellites; Correlation; Feature extraction; Streaming media; Kernel; Histogram of oriented gradient (HOG); kernelized correlation filter (KCF); optical flow (OF); ridge regression; satellite video tracking
类别
资金
- National Natural Science Foundation of China [41431175, 61822113, 41871243]
- Natural Science Foundation of Hubei Province [2018CFA050]
- National Key Research and Development Program of China [2018YFA0605501]
Despite the great success of correlation filter-based trackers in visual tracking, it is questionable whether they can still perform on the satellite video data, acquired by a satellite or space station very high above the earth. The difficulty lies in that the targets usually occupy only a few pixels compared with the image size of over one million pixels and almost melt into the similar background. Since correlation filter models strongly depend on the quality of features and the spatial layout of the tracked object, they would probably fail on satellite video tracking tasks. In this paper, we propose a hybrid kernel correlation filter (HKCF) tracker employing two complementary features adaptively in a ridge regression framework. One feature is the optical flow that can detect variation pixels of the target. The other one is the histogram of oriented gradient that can capture the contour and texture information in the target, and an adaptive fusion strategy is proposed to employ the strengths of both features in different satellite videos. Quantitative evaluations are performed on six real satellite video data sets. The results show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames/s.
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