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Article

Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model

1
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Satellite Image Geometric Correction Processing of Chang Guang Satellite Technology Co., Ltd., Changchun 130051, China
3
School of Geoscience, Yangtze University, Wuhan 430100, China
4
School of Computer and Engineering, Xi’an University of Technology, No. 5 Jin Hua South Road, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Academic Editor: Edoardo Pasolli
Received: 11 March 2021 / Revised: 25 March 2021 / Accepted: 26 March 2021 / Published: 29 March 2021
Satellite video single object tracking has attracted wide attention. The development of remote sensing platforms for earth observation technologies makes it increasingly convenient to acquire high-resolution satellite videos, which greatly accelerates ground target tracking. However, overlarge images with small object size, high similarity among multiple moving targets, and poor distinguishability between the objects and the background make this task most challenging. To solve these problems, a deep Siamese network (DSN) incorporating an interframe difference centroid inertia motion (ID-CIM) model is proposed in this paper. In object tracking tasks, the DSN inherently includes a template branch and a search branch; it extracts the features from these two branches and employs a Siamese region proposal network to obtain the position of the target in the search branch. The ID-CIM mechanism was proposed to alleviate model drift. These two modules build the ID-DSN framework and mutually reinforce the final tracking results. In addition, we also adopted existing object detection datasets for remotely sensed images to generate training datasets suitable for satellite video single object tracking. Ablation experiments were performed on six high-resolution satellite videos acquired from the International Space Station and “Jilin-1” satellites. We compared the proposed ID-DSN results with other 11 state-of-the-art trackers, including different networks and backbones. The comparison results show that our ID-DSN obtained a precision criterion of 0.927 and a success criterion of 0.694 with a frames per second (FPS) value of 32.117 implemented on a single NVIDIA GTX1070Ti GPU. View Full-Text
Keywords: satellite video; single object tracking; Siamese network; model drift; object detection; remote sensing satellite video; single object tracking; Siamese network; model drift; object detection; remote sensing
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MDPI and ACS Style

Zhu, K.; Zhang, X.; Chen, G.; Tan, X.; Liao, P.; Wu, H.; Cui, X.; Zuo, Y.; Lv, Z. Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model. Remote Sens. 2021, 13, 1298. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071298

AMA Style

Zhu K, Zhang X, Chen G, Tan X, Liao P, Wu H, Cui X, Zuo Y, Lv Z. Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model. Remote Sensing. 2021; 13(7):1298. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071298

Chicago/Turabian Style

Zhu, Kun, Xiaodong Zhang, Guanzhou Chen, Xiaoliang Tan, Puyun Liao, Hongyu Wu, Xiujuan Cui, Yinan Zuo, and Zhiyong Lv. 2021. "Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model" Remote Sensing 13, no. 7: 1298. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071298

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