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A Collaborative Learning Tracking Network for Remote Sensing Videos

Li, Xiaotong; Jiao, Licheng; Zhu, Hao; Liu, Fang; Yang, Shuyuan; Zhang, Xiangrong; Wang, Shuang; Qu, Rong


Xiaotong Li

Licheng Jiao

Hao Zhu

Fang Liu

Shuyuan Yang

Xiangrong Zhang

Shuang Wang

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Professor of Computer Science


With the increasing accessibility of remote sensing videos, remote sensing tracking is gradually becoming a hot issue. However, accurately detecting and tracking in complex remote sensing scenes is still a challenge. In this article, we propose a collaborative learning tracking network for remote sensing videos, including a consistent receptive field parallel fusion module (CRFPF), dual-branch spatial-channel co-attention (DSCA) module, and geometric constraint retrack strategy (GCRT). Considering the small-size objects of remote sensing scenes are difficult for general forward networks to extract effective features, we propose a CRFPF-module to establish parallel branches with consistent receptive fields to separately extract from shallow to deep features and then fuse hierarchical features adaptively. Since the objects and their background are difficult to distinguish, the proposed DSCA-module uses the spatial-channel co-attention mechanism to collaboratively learn the relevant information, which enhances the saliency of the objects and regresses to precise bounding boxes. Considering the interference of similar objects, we designed a GCRT-strategy to judge whether there is a false detection through the estimated motion trajectory and then recover the correct object by weakening the feature response of interference. The experimental results and theoretical analysis on multiple datasets demonstrate our proposed method’s feasibility and effectiveness. Code and net are available at


Li, X., Jiao, L., Zhu, H., Liu, F., Yang, S., Zhang, X., …Qu, R. (2023). A Collaborative Learning Tracking Network for Remote Sensing Videos. IEEE Transactions on Cybernetics, 53(3), 1954-1967.

Journal Article Type Article
Acceptance Date Jul 7, 2022
Online Publication Date Jul 7, 2022
Publication Date 2023
Deposit Date Jul 9, 2022
Publicly Available Date Jul 8, 2024
Journal IEEE Transactions on Cybernetics
Electronic ISSN 2168-2275
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 53
Issue 3
Pages 1954-1967
Keywords Electrical and Electronic Engineering; Computer Science Applications; Human-Computer Interaction; Information Systems; Control and Systems Engineering; Software
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