论文标题
部分可观测时空混沌系统的无模型预测
CoCoLoT: Combining Complementary Trackers in Long-Term Visual Tracking
论文作者
论文摘要
如何结合不同算法的合奏的互补功能在视觉对象跟踪中具有核心兴趣。已经实现了这种问题的重大进展，但是考虑短期跟踪方案。取而代之的是，解决方案已经忽略了长期跟踪设置。在本文中，我们明确考虑了长期跟踪方案，并提供了一个名为CoColot的框架，该框架结合了互补视觉跟踪器的特征，以实现增强的长期跟踪性能。 CoColot认为跟踪器是否通过在线学习的深入验证模型遵循目标对象，因此激活了选择最佳性能跟踪器的决策策略，并纠正了失败的跟踪器的性能。对所提出的方法进行了广泛的评估，并与其他几种解决方案进行了比较，表明它与最流行的长期视觉跟踪基准的最先进竞争。
How to combine the complementary capabilities of an ensemble of different algorithms has been of central interest in visual object tracking. A significant progress on such a problem has been achieved, but considering short-term tracking scenarios. Instead, long-term tracking settings have been substantially ignored by the solutions. In this paper, we explicitly consider long-term tracking scenarios and provide a framework, named CoCoLoT, that combines the characteristics of complementary visual trackers to achieve enhanced long-term tracking performance. CoCoLoT perceives whether the trackers are following the target object through an online learned deep verification model, and accordingly activates a decision policy which selects the best performing tracker as well as it corrects the performance of the failing one. The proposed methodology is evaluated extensively and the comparison with several other solutions reveals that it competes favourably with the state-of-the-art on the most popular long-term visual tracking benchmarks.
