EAAI Journal 2026 Journal Article
Progressive feature learning framework with alternate fusion for object tracking based on visible and thermal infrared images
- Xiaoyu Ni
- Sijia Peng
- Liang Yuan
- Rihan Hai
- Yadong Wang
- Yongming Han
Object tracking based on the fusion of visible light and thermal infrared has attracted increasing attention because two modalities provide complementary information for robust tracking in challenging environments. However, how to effectively exploit multimodal context and promote sufficient interaction between modality-specific and modality-shared information remains a key challenge. In this work, we propose a progressive feature learning framework based on a vision Transformer backbone for object tracking using visible light and thermal infrared fusion. The proposed method introduces a spatial feature adaptive learning fusion module to produce fused multimodal features by learning spatially varying modality weights, and a cross-fusion discrimination module to enhance target–background discrimination by refining fused template and search representations. Different from conventional progressive fusion pipelines that mainly emphasize repeated modality fusion, our framework follows an alternating progressive learning strategy that repeatedly switches between modality-complementary fusion and tracking-oriented discrimination refinement across network depth. In this way, modality-specific information and modality-shared information are collaboratively optimized at different semantic levels and stages. Extensive experiments on standard tracking benchmarks demonstrate that the proposed method achieves competitive accuracy and efficiency compared with recent advanced trackers. These results indicate that the proposed method is suitable for artificial intelligence applications requiring reliable object tracking in real-world scenarios under low illumination, partial occlusion, and adverse conditions, such as intelligent surveillance, transportation monitoring, unmanned system perception, search and rescue, and safety-critical observation.