Visual object tracking is a crucial area of computer vision research. It aims to accurately track objects in videos with challenges such as occlusion, deformation, and lighting variations. Existing algorithms face difficulties when objects leave the camera or reappear after being occluded, and they also struggle to track objects with significant appearance changes. To address these issues, this study proposed a novel tracking algorithm. It combines tracking and detection, enabling global searching when objects are occluded or disappear and redetecting them when they reappear. A multi-template updating mechanism was used to adapt to changes in appearance. The study proposed the OTB100-AB and UAV123-AB datasets to evaluate the tracker's ability to handle target disappearance, along with the TNR metric. The proposed algorithm was evaluated on these datasets, as were OTB50 and UAV20L, which outperformed state-of-the-art algorithms and significantly improved tracking performance.

中文翻译：

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基于多模板更新Siamese网络的遮挡感知视觉目标跟踪

视觉对象跟踪是计算机视觉研究的一个重要领域。它的目标是准确跟踪视频中的对象，并应对遮挡、变形和光照变化等挑战。当物体离开相机或被遮挡后重新出现时，现有算法面临困难，并且它们也难以跟踪具有显着外观变化的物体。为了解决这些问题，本研究提出了一种新颖的跟踪算法。它结合了跟踪和检测，当物体被遮挡或消失时能够进行全局搜索，并在物体重新出现时重新检测它们。采用多模板更新机制来适应外观的变化。该研究提出了 OTB100-AB 和 UAV123-AB 数据集来评估跟踪器处理目标消失的能力以及 TNR 指标。所提出的算法在这些数据集上进行了评估，OTB50 和 UAV20L 也是如此，其性能优于最先进的算法，并显着提高了跟踪性能。