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

Each sequence in existing RGBT tracking datasets is typically captured from a single platform equipped with both RGB (visible light) and TIR (thermal infrared) sensors. In real-world applications, tracking some objects requires cross-platform collaboration and these platforms might be equipped with different sensors. However, changes in modalities and platforms may cause significant variations in target appearance and abrupt position shifts, which existing RGBT trackers struggle to handle. To address these challenges, we define a new task, termed dynamic RGBT tracking, focusing on cross-platform and modality-variant scenarios. Considering the dynamic changes of modalities and platforms, we investigate dynamic RGBT tracking from a causal perspective, and assume that images consist of causal factors (target-relevant information) and non-causal factors (target-irrelevant information, i.e., modality/platform information), where only the former is conducive to stable tracking. Based on this assumption, we propose a novel causality-based modality&platform-invariant representation learning approach to capture robust invariant representations for dynamic RGBT tracking. In particular, to mitigate the challenges posed by modality variations, we design a causal consistency encoder that introduces an intervener to model feature uncertainty and simulate modal variations, compelling the model to focus on modality-invariant features to improve tracking robustness. To overcome the issue of abrupt view change and position shift, we design a platform-independent global searcher to re-localize the target whenever a platform switch occurs, which leverages an intervener to simulate the interference of platform changes on features, encouraging the searcher to learn platform-invariant representations for improved localization accuracy. In addition, to promote the research and development of dynamic RGBT tracking, we construct a dataset named DRGBT603, which consists of 603 sequences with a total of 1.49 M frame pairs. Extensive experiments on DRGBT603 dataset validate the effectiveness of the proposed method against other state-of-the-art methods. Our code and data are now available: https://github.com/dongdong2061/DRGBT.