  Due to the different photosensitive properties of infrared and visible light,
the registered RGB-T image pairs shot in the same scene exhibit quite different
characteristics. 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 whose filters for each layer are partially coupled to
fuse the features extracted from infrared images and visible light images. 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
under various light conditions. Experiments on two RGB-T tracking benchmark
datasets demonstrate that the proposed SiamIVFN has achieved state-of-the-art
performance. The tracking speed of SiamIVFN is 147.6FPS, the current fastest
RGB-T fusion tracker.
