A single object tracking model with hierarchical RGBT feature interaction

Due to the limitations of traditional single-modal object tracking methods under challenging conditions, RGBT single-object tracking has gained attention for its robustness. In this paper, we introduce a new RGBT object tracking model, SiamTFF, which utilizes a dual-modal feature interaction network to efficiently harness complementary features from both visible and thermal infrared information, thereby enhancing the model's ability to track objects under various challenging conditions, such as low-light or background clutter. Additionally, we have designed a multi-level cross-correlation operation structure, enabling the model to effectively track targets of various sizes. SiamTFF based on a siamese network framework, which balances speed and performance more effectively compared to models based on transformer and MDNet frameworks. We trained and tested SiamTFF on public datasets. The tracking results indicate that our model achieves high precision and success rates in real-world tracking scenarios while effectively balancing performance and speed. This demonstrates the effectiveness and generalization capabilities of SiamTFF, as well as its ability to handle various challenging conditions efficiently.