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Due to the different photosensitive properties of infrared and visible light, infrared and visible light images have individual features. However, since the registered RGB-T image pairs shot in the same scene, they also contain common features. This paper proposes a Siamese infrared and visible light fusion Network (SiamIVFN) for RBG-T image-based tracking. SiamIVFN contains two main subnetworks: a complementary-feature-fusion network (CFFN) and a contribution-aggregation network (CAN). CFFN utilizes a two-stream multilayer convolutional structure that separately extracts individual features, and filters in each layer are partially coupled to extract common features. CFFN is a feature-level fusion network, which can cope with the misalignment of the RGB-T image pairs. Through adaptively calculating the contributions of infrared and visible light features obtained from CFFN, CAN makes the tracker robust …