A robust RGBT tracking framework with temporal information enhancement and backward trajectory verification

Abstract RGBT (visible-thermal) object tracking holds significant value in complex scenarios such as low-light and hazy environments, enabling robust all-weather tracking by leveraging the complementary strengths of visible and thermal infrared modalities. However, challenges such as target appearance variations, similar object interference, and camera motion often lead to tracking drift. This paper proposes RecheckTrack, a robust RGBT tracking framework that addresses these issues through the enhancement of temporal information and a backward trajectory verification mechanism. The dual-branch fusion network adaptively learns target dynamics using appearance tokens and modality tokens. Modality tokens focus on high-quality features and target-probable regions, while appearance tokens track dynamic changes in target appearance, improving robustness against deformation, occlusion, and scale variations. To mitigate drift caused by sudden target or camera motion, a recheck network is introduced, which employs a two-stage candidate box selection method and jointly matches targets using bidirectional tracking consistency and appearance similarity. Additionally, for long-term tracking scenarios where targets may be lost, the recheck network is improved with a path-consistency-based backward trajectory selection method and an approximate global search strategy, efficiently recovering lost targets. Experiments on the VTUAV, LasHeR, and RGBT234 datasets demonstrate that RecheckTrack significantly reduces tracking drift and improves accuracy, providing an effective solution for RGBT tracking in complex scenarios.