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Nowadays, infrared target tracking has been a critical technology in thefield 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, trackingdrift often occurs when the tracker encounters infrared targets that vary insize or shape. To address this issue, we present a twofold structuredfeatures-based Siamese network for infrared target tracking. First of all, inorder to improve the discriminative capacity for infrared targets, a novelfeature fusion network is proposed to fuse both shallow spatial information anddeep semantic information into the extracted features in a comprehensivemanner. Then, a multi-template update module based on template update mechanismis designed to effectively deal with interferences from target appearancechanges which are prone to cause early tracking failures. Finally, bothqualitative and quantitative experiments are carried out on VOT-TIR 2016dataset, which demonstrates that our method achieves the balance of promisingtracking performance and real-time tracking speed against other out-of-the-arttrackers.

Twofold Structured Features-Based Siamese Network for Infrared Target Tracking