Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking

2026-05-21 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

SAMOSA is a new tracking framework designed to adapt the SAM 2 vision foundation model for complex visual object tracking (VOT) scenarios. Traditional VOT methods struggle with generalization, while direct SAM 2 application lacks explicit modeling for target motion dynamics, geometric consistency, and semantic consistency. SAMOSA addresses these limitations by integrating a lightweight nonlinear motion predictor to guide mask selection and memory filtering. It also leverages semantic cues for detecting target shifts and recovering from tracking failures, alongside geometric cues for structural constraints and improved stability. Extensive experiments demonstrate that SAMOSA consistently outperforms state-of-the-art SAM 2-based approaches on general benchmarks, shows stronger generalization than supervised VOT methods, and achieves substantial gains on anti-UAV datasets, which represent complex nonlinear motion challenges.

Key takeaway

For Computer Vision Engineers developing robust object trackers, SAMOSA's approach of adapting SAM 2 with explicit motion, geometry, and semantic cues offers a superior framework. You should explore integrating these explicit modeling techniques to overcome generalization limits and improve performance in complex, nonlinear scenarios, especially for anti-UAV applications. Consider the open-source code for practical implementation.

Key insights

SAMOSA adapts SAM 2 for robust visual object tracking by integrating explicit motion, geometry, and semantic cues.

Principles

• Foundation models require task-specific adaptation.
• Explicit motion modeling enhances tracking stability.
• Semantic and geometric cues improve recovery and consistency.

Method

SAMOSA employs a lightweight nonlinear motion predictor for mask guidance and memory filtering. It uses semantic cues for target shift detection and recovery, and geometric cues as structural constraints to boost tracking stability.

In practice

• Integrate motion dynamics into foundation models.
• Use semantic cues for tracking failure recovery.
• Apply geometric constraints for stability.

Topics

• Visual Object Tracking
• SAMOSA
• SAM 2
• Motion Prediction
• Semantic Adaptation
• Geometric Constraints
• Anti-UAV Tracking

Code references

• DurYi/SAMOSA

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.