A reliable tracker has the ability to adapt to change of objects over time, and is robust and accurate. We build such a tracker by extracting semantic features using robust Siamese networks and multi-granularity color features. It incorporates a semantic model that can capture high quality semantic features and an appearance model that can describe object at pixel, local and global levels effectively. Furthermore, we propose a novel selective traverse algorithm to allocate weights to semantic models and appearance models dynamically for better tracking performance. During tracking, our tracker updates appearance representations for objects based on the recent tracking results. The proposed tracker operates at speeds that exceed the real-time requirement, and outperforms nearly all other state-of-the-art trackers on OTB-2013/2015 and VOT-2016/2017 benchmarks.

可靠的跟踪器具有适应对象随时间变化的能力，并且功能强大且准确。我们通过使用健壮的暹罗网络和多粒度颜色特征提取语义特征来构建这样的跟踪器。它结合了可以捕获高质量语义特征的语义模型和可以有效描述像素，局部和全局级别的对象的外观模型。此外，我们提出了一种新颖的选择性遍历算法，可以为语义模型和外观模型动态分配权重，以实现更好的跟踪性能。在跟踪期间，我们的跟踪器会根据最近的跟踪结果来更新对象的外观表示。建议的跟踪器以超过实时要求的速度运行，