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Causality-Based Modality- and Platform-Invariant Representation Learning for Dynamic RGBT Tracking and a Benchmark.

Zhaodong Ding, Chenglong Li, Shengqing Miao

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 20, 2026

Summary

This study introduces dynamic RGB-Thermal (RGBT) tracking for cross-platform scenarios. A new causality-based method learns invariant representations to handle appearance and position shifts, improving tracking robustness.

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Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

Existing RGBT tracking datasets use single platforms, limiting real-world cross-platform applications.
Cross-platform tracking faces challenges from modality and platform changes, causing appearance variations and position shifts.
Current RGBT trackers struggle with these dynamic variations.

Purpose of the Study:

Define and address the new task of dynamic RGBT tracking for cross-platform, modality-variant scenarios.
Develop a causality-based approach to learn robust, invariant representations for dynamic RGBT tracking.
Improve tracking performance in complex real-world scenarios involving sensor and platform heterogeneity.

Main Methods:

Propose a causality-based representation learning approach using causal factors (target-relevant) and non-causal factors (modality/platform information).

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Design a causal consistency encoder with an intervener to simulate modal variations and learn modality-invariant features.

Develop a platform-independent global searcher with an intervener to handle platform switches and improve localization accuracy.

Main Results:

The proposed method demonstrates superior performance against state-of-the-art methods on the new DRGBT603 dataset.
The causality-based approach effectively mitigates challenges from modality and platform variations.
The developed dataset (DRGBT603) facilitates further research in dynamic RGBT tracking.

Conclusions:

The causality-based approach provides robust invariant representations for dynamic RGBT tracking.
The proposed method significantly enhances tracking robustness and localization accuracy in cross-platform scenarios.
The new dataset and method advance the field of dynamic RGBT tracking.