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

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Updated: Sep 16, 2025

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Improvement of SAM2 Algorithm Based on Kalman Filtering for Long-Term Video Object Segmentation.

Jun Yin1, Fei Wu1, Hao Su2

  • 1School of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

SAM2Plus enhances video object segmentation by integrating Kalman filters and adaptive memory management. This improves long-term tracking stability and accuracy, especially in challenging conditions like occlusions.

Keywords:
Kalman filterLVOSSA-VSAM 2long-term video

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Segment Anything Model 2 (SAM2) excels at pixel-level segmentation but struggles with long-term video tracking due to its static inference and fixed temporal window.
  • Error propagation and tracking instability occur in SAM2 with fast-moving objects, occlusions, or crowded scenes.

Purpose of the Study:

  • To introduce SAM2Plus, a zero-shot enhancement framework designed to overcome SAM2's limitations in long-term video object segmentation.
  • To improve tracking accuracy, stability, and robustness in dynamic and challenging visual environments.

Main Methods:

  • Integration of Kalman filter prediction for motion modeling and trajectory refinement.
  • Implementation of dynamic quality thresholds and multi-criteria evaluation for adaptive frame selection.
  • Development of an optimized memory system with adaptive pruning and context retention.

Main Results:

  • SAM2Plus demonstrated superiority over SAM2, achieving an average improvement of 1.0 in J&F metrics.
  • Significant gains exceeding 2.3 points were observed on SA-V and LVOS datasets for long-term tracking.
  • Real-time performance and strong generalization were achieved without fine-tuning, effectively handling occlusions and viewpoint changes.

Conclusions:

  • SAM2Plus effectively unifies motion-aware prediction with spatial segmentation, bridging static and dynamic reasoning gaps.
  • The framework offers a scalable solution for real-world applications like autonomous driving and surveillance.
  • SAM2Plus provides enhanced robustness and accuracy for video object segmentation tasks.