UIMM-Tracker: IMM-Based with Uncertainty Detection for Video Satellite Infrared Small-Target Tracking

Huang, Yuanxin; Zhi, Xiyang; Xu, Zhichao; Chen, Wenbin; Han, Qichao; Hu, Jianming; Sui, Yi; Zhang, Wei

doi:10.3390/rs17122052

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Open AccessArticle

UIMM-Tracker: IMM-Based with Uncertainty Detection for Video Satellite Infrared Small-Target Tracking

by

Yuanxin Huang

,

Xiyang Zhi

,

Zhichao Xu

,

Wenbin Chen

^*

,

Qichao Han

,

Jianming Hu

,

Yi Sui

and

Wei Zhang

Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2025, 17(12), 2052; https://doi.org/10.3390/rs17122052 (registering DOI)

Submission received: 29 April 2025 / Revised: 10 June 2025 / Accepted: 11 June 2025 / Published: 14 June 2025

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Abstract

Infrared video satellites have the characteristics of wide-area long-duration surveillance, enabling continuous operation day and night compared to visible light imaging methods. Therefore, they are widely used for continuous monitoring and tracking of important targets. However, energy attenuation caused by long-distance radiation transmission reduces imaging contrast and leads to the loss of edge contours and texture details, posing significant challenges to target tracking algorithm design. This paper proposes an infrared small-target tracking method, the UIMM-Tracker, based on the tracking-by-detection (TbD) paradigm. First, detection uncertainty is measured and injected into the multi-model observation noise, transferring the distribution knowledge of the detection process to the tracking process. Second, a dynamic modulation mechanism is introduced into the Markov transition process of multi-model fusion, enabling the tracking model to autonomously adapt to targets with varying maneuvering states. Additionally, detection uncertainty is incorporated into the data association method, and a distance cost matrix between trajectories and detections is constructed based on scale and energy invariance assumptions, improving tracking accuracy. Finally, the proposed method achieves average performance scores of 68.5%, 45.6%, 56.2%, and 0.41 in IDF1, MOTA, HOTA, and precision metrics, respectively, across 20 challenging sequences, outperforming classical methods and demonstrating its effectiveness.

Keywords: infrared target tracking; interactive multiple model (IMM); tracking by detection; uncertainty of detection

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MDPI and ACS Style

Huang, Y.; Zhi, X.; Xu, Z.; Chen, W.; Han, Q.; Hu, J.; Sui, Y.; Zhang, W. UIMM-Tracker: IMM-Based with Uncertainty Detection for Video Satellite Infrared Small-Target Tracking. Remote Sens. 2025, 17, 2052. https://doi.org/10.3390/rs17122052

AMA Style

Huang Y, Zhi X, Xu Z, Chen W, Han Q, Hu J, Sui Y, Zhang W. UIMM-Tracker: IMM-Based with Uncertainty Detection for Video Satellite Infrared Small-Target Tracking. Remote Sensing. 2025; 17(12):2052. https://doi.org/10.3390/rs17122052

Chicago/Turabian Style

Huang, Yuanxin, Xiyang Zhi, Zhichao Xu, Wenbin Chen, Qichao Han, Jianming Hu, Yi Sui, and Wei Zhang. 2025. "UIMM-Tracker: IMM-Based with Uncertainty Detection for Video Satellite Infrared Small-Target Tracking" Remote Sensing 17, no. 12: 2052. https://doi.org/10.3390/rs17122052

APA Style

Huang, Y., Zhi, X., Xu, Z., Chen, W., Han, Q., Hu, J., Sui, Y., & Zhang, W. (2025). UIMM-Tracker: IMM-Based with Uncertainty Detection for Video Satellite Infrared Small-Target Tracking. Remote Sensing, 17(12), 2052. https://doi.org/10.3390/rs17122052

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UIMM-Tracker: IMM-Based with Uncertainty Detection for Video Satellite Infrared Small-Target Tracking

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