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OITrack: Multi-Object Tracking for Small Targets in Satellite Video via Online Trajectory Completion and Iterative Expansion over Union.

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Title: OITrack: Multi-Object Tracking for Small Targets in Satellite Video via Online Trajectory Completion and Iterative Expansion over Union.
Authors: Lu, Weishan1 (AUTHOR), Wang, Xueying1 (AUTHOR) wangxueying@nudt.edu.cn, An, Wei1 (AUTHOR), Xiao, Chao1 (AUTHOR), Yin, Qian1 (AUTHOR), Zhang, Guoliang1 (AUTHOR)
Source: Remote Sensing. Jun2025, Vol. 17 Issue 12, p2042. 22p.
Subjects: Microspacecraft, Kalman filtering, Experimental films, Adaptive filters, Streaming video & television
Abstract: Multi-object tracking (MOT) in satellite videos presents significant challenges, including small target sizes, dense distributions, and complex motion patterns. To address these issues, we propose OITrack, an improved tracking framework that integrates a Trajectory Completion Module (TCM), an Adaptive Kalman Filter (AKF), and an Iterative Expansion Intersection over Union Strategy (I-EIoU) strategy. Specifically, TCM enhances temporal continuity by compensating for missing trajectories, AKF improves tracking robustness by dynamically adjusting observation noise, and I-EIoU optimizes target association, leading to more accurate small-object matching. Experimental evaluations on the VIdeo Satellite Objects (VISO) dataset demonstrated that OITrack outperforms existing MOT methods across multiple key metrics, achieving a Multiple Object Tracking Accuracy (MOTA) of 57.0%, an Identity F1 Score (IDF1) of 67.5%, a reduction in False Negatives (FN) to 29,170, and a decrease in Identity Switches (ID switches) to 889. These results indicate that our method effectively improves tracking accuracy while minimizing identity mismatches, enhancing overall robustness. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: OITrack: Multi-Object Tracking for Small Targets in Satellite Video via Online Trajectory Completion and Iterative Expansion over Union.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2025, Vol. 17 Issue 12, p2042. 22p.
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  Data: <searchLink fieldCode="DE" term="%22Microspacecraft%22">Microspacecraft</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+films%22">Experimental films</searchLink><br /><searchLink fieldCode="DE" term="%22Adaptive+filters%22">Adaptive filters</searchLink><br /><searchLink fieldCode="DE" term="%22Streaming+video+%26+television%22">Streaming video & television</searchLink>
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  Data: Multi-object tracking (MOT) in satellite videos presents significant challenges, including small target sizes, dense distributions, and complex motion patterns. To address these issues, we propose OITrack, an improved tracking framework that integrates a Trajectory Completion Module (TCM), an Adaptive Kalman Filter (AKF), and an Iterative Expansion Intersection over Union Strategy (I-EIoU) strategy. Specifically, TCM enhances temporal continuity by compensating for missing trajectories, AKF improves tracking robustness by dynamically adjusting observation noise, and I-EIoU optimizes target association, leading to more accurate small-object matching. Experimental evaluations on the VIdeo Satellite Objects (VISO) dataset demonstrated that OITrack outperforms existing MOT methods across multiple key metrics, achieving a Multiple Object Tracking Accuracy (MOTA) of 57.0%, an Identity F1 Score (IDF1) of 67.5%, a reduction in False Negatives (FN) to 29,170, and a decrease in Identity Switches (ID switches) to 889. These results indicate that our method effectively improves tracking accuracy while minimizing identity mismatches, enhancing overall robustness. [ABSTRACT FROM AUTHOR]
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  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17122042
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      – SubjectFull: Experimental films
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      – TitleFull: OITrack: Multi-Object Tracking for Small Targets in Satellite Video via Online Trajectory Completion and Iterative Expansion over Union.
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              M: 06
              Text: Jun2025
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