  Nowadays, infrared target tracking has been a critical technology in the
field of computer vision and has many applications, such as motion analysis,
pedestrian surveillance, intelligent detection, and so forth. Unfortunately,
due to the lack of color, texture and other detailed information, tracking
drift often occurs when the tracker encounters infrared targets that vary in
size or shape. To address this issue, we present a twofold structured
features-based Siamese network for infrared target tracking. First of all, in
order to improve the discriminative capacity for infrared targets, a novel
feature fusion network is proposed to fuse both shallow spatial information and
deep semantic information into the extracted features in a comprehensive
manner. Then, a multi-template update module based on template update mechanism
is designed to effectively deal with interferences from target appearance
changes which are prone to cause early tracking failures. Finally, both
qualitative and quantitative experiments are carried out on VOT-TIR 2016
dataset, which demonstrates that our method achieves the balance of promising
tracking performance and real-time tracking speed against other out-of-the-art
trackers.
