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HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion.

Wu, Yuqi; Zhang, Xiaowen; Liu, Qiaoyuan; Xue, Donglin; Sun, Haijiang; Ren, Jinchang


Yuqi Wu

Xiaowen Zhang

Qiaoyuan Liu

Donglin Xue

Haijiang Sun


Amir Hussain

Iman Yi Liao

Rongjun Chen

Kaizhu Huang

Huimin Zhao

Xiaoyong Liu

Thomas Maul


Multi-object tracking in satellite videos (SV-MOT) is one of the most challenging tasks in remote sensing, its difficulty mainly comes from the low spatial resolution, small target and extremely complex background. The widely studied multi-object tracking (MOT) approaches for general images can hardly be directly introduced to the remote sensing scenarios. The main reason can be attributed to: 1) the existing MOT approaches would cause a significant missed detection of the small targets in satellite videos; 2) it is difficult for the general MOT approaches to generate complete trajectories in complex satellite scenarios. To address these problems, a novel SV-MOT approach enhanced by high-resolution feature fusion (HRMOT) is proposed. It is comprised of a high-resolution detection network and a two-step based association strategy. In the high-resolution detection network, a high-resolution feature fusion module is designed to assist the detection by maintaining small object features in forward propagation. Based on high-quality detection, the densely-packed weak objects can be effectively tracked by associating almost every detection box instead of only the high score ones. Comprehensive experiment results on the representative satellite video datasets (VISO) demonstrate that the proposed HRMOT can achieve a competitive performance on the tracking accuracy and the frequency of ID conversion with the state-of-the-art (SOTA) methods.


WU, Y., ZHANG, X., LIU, Q., XUE, D., SUN, H. and REN, J. 2024. HRMOT: two-step association based multi-object tracking in satellite videos enhanced by high-resolution feature fusion. In: Ren, J., Hussain, A., Liao, I.Y. et al. (eds.) Advances in brain inspired cognitive systems: proceedings of the 13th Brain-inspired cognitive systems 2023 (BICS 2023), 5-6 August 2023, Kuala Lumpur, Malaysia. Lecture notes in computer sciences, 14374. Cham: Springer [online], pages 251-263. Available from:

Conference Name 13th Brain-inspired cognitive systems international conference 2023 (BICS 2023)
Conference Location Kuala Lumpur, Malaysia
Start Date Aug 5, 2023
End Date Aug 6, 2023
Acceptance Date Jul 28, 2023
Online Publication Date May 22, 2024
Publication Date Dec 31, 2024
Deposit Date Jun 14, 2024
Publicly Available Date May 23, 2025
Publisher Springer
Pages 251-263
Series Title Lecture notes in computer science (LNCS)
Series Number 14374
Series ISSN 0302-9743; 1611-3349
Book Title Advances in brain inspired cognitive systems
ISBN 9789819714162
Keywords Data association; High-resolution feature fusion; Multi-object tracking; Satellite video
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