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Chen Guanzhou


Main positions:助理研究员
Gender:Male
Status:Employed
School/Department:测绘遥感信息工程国家重点实验室
  • Discipline: Photogrammetry and Remote Sensing
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    Current position: Home >> Scientific Research >> Paper Publications

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

    Hits : Praise

    DOI number:10.3390/rs13071298

    Journal:REMOTE SENSING

    Key Words:satellite video,single object tracking,Siamese network,model drift,object detection,remote sensing

    Abstract: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.

    Co-author:Guanzhou Chen,Xiaoliang Tan,Puyun Liao,Hongyu Wu,Xiujuan Cui,Yinan Zuo,Zhiyong Lv

    Indexed by:Journal paper

    Correspondence Author:Xiaodong Zhang

    Document Type:J

    Volume:13

    Issue:7

    Translation or Not:no

    Date of Publication:2021-03-29

    Included Journals:SCI