Recent progresses in visual tracking have greatly improved the tracking performance. However, challenges such as occlusion and view change remain obstacles in real world deployment. A natural solution to these challenges is to use multiple cameras with multiview inputs, though existing systems are mostly limited to specific targets ( e.g. human), static cameras, and/or require camera calibration. To break through these limitations, we propose a generic multiview tracking (GMT) framework that allows camera movement, while requiring neither specific object model nor camera calibration. A key innovation in our framework is a cross-camera trajectory prediction network (TPN), which implicitly and dynamically encodes camera geometric relations, and hence addresses missing target issues such as occlusion. Moreover, during tracking, we assemble information across different cameras to dynamically update a novel collaborative correlation filter (CCF), which is shared among cameras to achieve robustness against view change. The two components are integrated into a correlation filter tracking framework, where features are trained offline using existing single view tracking datasets. For evaluation, we first contribute a new generic multiview tracking dataset (GMTD) with careful annotations, and then run experiments on the GMTD and CAMPUS datasets. The proposed GMT algorithm shows clear advantages in terms of robustness over state-of-the-art ones.

中文翻译：

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具有多视图轨迹预测的视觉跟踪。

视觉跟踪的最新进展极大地提高了跟踪性能。但是，诸如遮挡和视角变化之类的挑战仍然是现实世界部署中的障碍。这些挑战的自然解决方案是使用多摄像机输入多视点，尽管现有系统大多限于特定目标（ 例如人类），静态相机和/或需要相机校准。为了突破这些限制，我们建议通用多视图跟踪（GMT）框架，它允许摄像机移动，同时不需要特定的对象模型或摄像机校准。跨相机是我们框架中的一项关键创新轨迹预测网络（TPN），它隐式且动态地编码了相机的几何关系，因此解决了诸如遮挡之类的目标缺失问题。此外，在跟踪过程中，我们会在不同的摄像机之间收集信息，以动态更新小说协同相关过滤器（CCF），在相机之间共享以实现针对视图变化的鲁棒性。这两个组件被集成到相关过滤器跟踪框架中，其中使用现有的单视图跟踪数据集对功能进行脱机训练。为了进行评估，我们首先贡献一个新的通用多视图跟踪数据集（GMTD）进行仔细注释，然后在GMTD和CAMPUS数据集上进行实验。所提出的GMT算法在鲁棒性方面优于最先进的算法。