  For both visible and infrared images have their own advantages and
disadvantages, RGBT tracking has attracted more and more attention. The key
points of RGBT tracking lie in feature extraction and feature fusion of visible
and infrared images. Current RGBT tracking methods mostly pay attention to both
individual features (features extracted from images of a single camera) and
common features (features extracted and fused from an RGB camera and a thermal
camera), while pay less attention to the different and dynamic contributions of
individual features and common features for different sequences of registered
image pairs. This paper proposes a novel RGBT tracking method, called Dynamic
Fusion Network (DFNet), which adopts a two-stream structure, in which two
non-shared convolution kernels are employed in each layer to extract individual
features. Besides, DFNet has shared convolution kernels for each layer to
extract common features. Non-shared convolution kernels and shared convolution
kernels are adaptively weighted and summed according to different image pairs,
so that DFNet can deal with different contributions for different sequences.
DFNet has a fast speed, which is 28.658 FPS. The experimental results show that
when DFNet only increases the Mult-Adds of 0.02% than the
non-shared-convolution-kernel-based fusion method, Precision Rate (PR) and
Success Rate (SR) reach 88.1% and 71.9% respectively.
