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Volume 25
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10.3390/s25165131
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Correction to Sensors 2025, 25(13), 4199.
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Correction

Correction: Yin et al. Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation. Sensors 2025, 25, 4199

by
Jun Yin
1,
Fei Wu
1,
Hao Su
2,*,
Peng Huang
2 and
Yuetong Qixuan
2
1
School of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
2
Zhejiang Dahua Technology Co., Ltd., Hangzhou 310053, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(16), 5131; https://doi.org/10.3390/s25165131 (registering DOI)
Submission received: 24 July 2025 / Accepted: 29 July 2025 / Published: 19 August 2025
(This article belongs to the Section Sensing and Imaging)
Versions Notes
In the original publication [1], there were some errors in the main text. The corrected content appears below.
1. Introduction
Paragraph 2: “visual object tracking (VOT)” is replaced by “VOS”.
Paragraph 3: “VOT” is replaced by “visual object tracking (VOT)”.
Paragraph 4: “tracking performance in VOT scenarios.” is replaced by “segmentation performance in VOS scenarios.”
Paragraph 6: ‘tracking’ is replaced by a ‘segmentation’.
3.2. Kalman Filtering-IoU Fusion Framework
Paragraph 1: “To improve tracking consistency, we propose the Kalman Filtering-IoU (KF-IoU) fusion framework, which introduces a predictive component to stabilize tracking and reduce the impact of single-frame errors.” is replaced by a “Given the success of SAMURAI [5] in the VOT task, to improve tracking consistency in VOS scenarios, we propose the Kalman Filtering-IoU (KF-IoU) fusion framework, which introduces a predictive component via Kalman Filtering to stabilize tracking and reduce the impact of single-frame errors while ensuring spatial segmentation precision through IoU optimization.”
Paragraph 6: Add “Following [5], the KF-IoU fusion function is defined as follows:”
3.3. Adaptive Historical Frame Selection Strategy Based on Dynamic Threshold
Paragraph 4: “The validity of the historical frame fi is determined by the following conditions simultaneously:” is replaced by “The validity of the historical frame fi is determined by the following conditions simultaneously [5]:”
6. Conclusions
Paragraph 1: ‘tracking’ is replaced by a ‘segmentation’.
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Yin, J.; Wu, F.; Su, H.; Huang, P.; Qixuan, Y. Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation. Sensors 2025, 25, 4199. [Google Scholar] [CrossRef] [PubMed]
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Share and Cite

MDPI and ACS Style

Yin, J.; Wu, F.; Su, H.; Huang, P.; Qixuan, Y. Correction: Yin et al. Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation. Sensors 2025, 25, 4199. Sensors 2025, 25, 5131. https://doi.org/10.3390/s25165131

AMA Style

Yin J, Wu F, Su H, Huang P, Qixuan Y. Correction: Yin et al. Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation. Sensors 2025, 25, 4199. Sensors. 2025; 25(16):5131. https://doi.org/10.3390/s25165131

Chicago/Turabian Style

Yin, Jun, Fei Wu, Hao Su, Peng Huang, and Yuetong Qixuan. 2025. "Correction: Yin et al. Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation. Sensors 2025, 25, 4199" Sensors 25, no. 16: 5131. https://doi.org/10.3390/s25165131

APA Style

Yin, J., Wu, F., Su, H., Huang, P., & Qixuan, Y. (2025). Correction: Yin et al. Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation. Sensors 2025, 25, 4199. Sensors, 25(16), 5131. https://doi.org/10.3390/s25165131

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