High-resolution satellite video single object tracking based on thicksiam framework

ABSTRACT

High-resolution satellite videos realize the short-dated gaze observation of the designated area on the ground, and its emergence has improved the temporal resolution of remote sensing data to the second level. Single object tracking (SOT) task in satellite video has attracted considerable attention. However, it faces challenges such as complex background, poor object feature representation, and lack of publicly available datasets. To cope with these challenges, a ThickSiam framework consisting of a Thickened Residual Block Siamese Network (TRBS-Net) for extracting robust semantic features to obtain the initial tracking results and a Remoulded Kalman Filter (RKF) module for simultaneously correcting the trajectory and size of the targets is designed in this work. The results of TRBS-Net and RKF modules are combined by an N-frame-convergence mechanism to achieve accurate tracking results. Ablation experiments are implemented on our annotated dataset to evaluate the performance of the proposed ThickSiam framework and other 19 state-of-the-art trackers. The comparison results show that our ThickSiam tracker obtains a precision value of 0.991 and a success value of 0.755 while running at 56.849 FPS implemented on one NVIDIA GTX1070Ti GPU.

KEYWORDS:

• High-resolution satellite videos
• single object tracking
• Siamese network
• Kalman filter

Acknowledgment

The authors would like to thank Deimos Imaging and Chang Guang Satellite Technology Co., Ltd. for acquiring and providing the satellite videos used in this paper. The authors would like to thank Prof. Gong Cheng, Prof. Junwei Han from School of Automation, Northwestern Polytechnical University, Xi’an for providing the awesome remote sensing object dataset DIOR. The authors would like to thank Microsoft for providing the awesome COCO dataset. The part numerical calculations in this paper are performed on the supercomputing system in the Supercomputing Center of Wuhan University.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The testing data in this study are available at https://github.com/CVEO/ThickSiam.

Notes

1. http://mall.charmingglobe.com/Sampledata.

2. https://ieee-dataport.org/open-access/data-fusion-contest-2016-dfc2016.

Additional information

Funding

This work was supported in part by National Natural Science Foundation of China (No. 42101346), in part by China Postdoctoral Science Foundation (No. 2020M680109), in part by Fundamental Research Funds for the Central Universities (No. 2042021kf0008), in part by Natural Resources Science and Technology Project of Hubei Province (No. ZRZY2021KJ01).