results: 在VOT-TIR 2016 dataset上进行了质量和量测试，并证明了我们的方法可以具有与其他状态革新跟踪器相当的跟踪性能和实时跟踪速度。Abstract
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.
摘要
现在，红外目标跟踪技术在计算机视觉领域具有重要意义，有很多应用，如运动分析、人员监测、智能检测等等。然而，由于红外目标缺乏颜色、文本和其他细节信息，跟踪偏移经常发生，当跟踪器遇到变形或大小不同的红外目标时。为解决这个问题，我们提出了一种两重结构特征基于Siamese网络的红外目标跟踪方法。首先，为了提高红外目标的抑制能力，我们提出了一种新的特征融合网络，将表层空间信息和深度 semantics信息融合到提取的特征中，以实现全面的特征融合。然后，我们设计了基于模板更新机制的多模板更新模块，以有效地处理目标外观变化所导致的跟踪失败。最后，我们对VOT-TIR 2016 dataset进行了质量和kvantitativerexperiment，结果显示，我们的方法可以协调出色的跟踪性和实时跟踪速度，与其他状态OF-THE-ART tracker相比。