Twofold Structured Features-Based Siamese Network for Infrared Target Tracking

Nowadays, infrared target tracking has been a critical technology in the field of computer vision and has many computational social system-related applications, such as urban security, pedestrian counting, smoke and fire detection, and so forth. Unfortunately, due to the absence of detailed information such as texture or color, it is easy for tracking drift to occur when the tracker encounters infrared targets that vary in shape or size. In order to address this issue, we present a twofold structured features-based Siamese network for infrared target tracking. Above all, a novel feature fusion network is proposed to make full use of both shallow spatial information and deep semantic information in a comprehensive manner, so as to improve the discriminative capacity for infrared targets. Then, a multitemplate update module 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 implemented on VOT-TIR 2016 and GTOT datasets, which demonstrates that our method achieves the balance of promising tracking performance and real-time tracking speed against other state-of-the-art trackers.