Most visual trackers focus on short-term tracking. The target is always in the camera field of view or slight occlusion (OCC). Compared with short-term tracking, long-term tracking is a more challenging task. It requires the ability to capture the target in long-term sequences and undergo frequent disappearances and reappearances of target. Therefore, long-term tracking is much closer to a realistic tracking system. However, few long-term tracking algorithms have been developed and few promising performances have been shown until now. We focus on a long-term visual tracking framework based on parts correlation filters (CFs). Our long-term tracking framework is composed of a part-based short-term tracker and a re-detection module. First, multiple CFs have been applied to locate the target collaboratively and address the partial OCC issue. Second, our method updates the part adaptively based on its motion similarity and reliability score to retain its robustness. Third, a switching strategy has been designed to dynamically activate the re-detection module and interact the search mode between local and global search. In addition, our re-detector is trained by sampling positive and negative samples around the reliable tracking target to adapt to the appearance changes. To evaluate the candidates from the re-detection module, verification has been carried out, which could ensure the precision of recovery. Numerous experimental results demonstrate that our proposed tracking method performs favorably against state-of-the-art methods in terms of accuracy and robustness.

中文翻译：

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使用多个相关滤波器进行可靠的基于零件的长期跟踪

大多数视觉跟踪器都专注于短期跟踪。目标始终在摄像机视场或轻微遮挡（OCC）中。与短期跟踪相比，长期跟踪是一项更具挑战性的任务。它需要能够长期捕获目标并频繁消失和重新出现目标的能力。因此，长期跟踪更接近于现实的跟踪系统。然而，到目前为止，很少开发长期跟踪算法，并且几乎没有显示出有希望的性能。我们专注于基于零件相关性过滤器（CF）的长期视觉跟踪框架。我们的长期跟踪框架由基于零件的短期跟踪器和重新检测模块组成。首先，已应用多个CF来协作定位目标并解决部分OCC问题。第二，我们的方法根据其运动相似性和可靠性得分自适应地更新零件，以保持其鲁棒性。第三，已经设计了一种切换策略来动态激活重新检测模块并在本地搜索和全局搜索之间交互搜索模式。另外，我们的再检测器经过训练，可以围绕可靠的跟踪目标对正负样本进行采样，以适应外观变化。为了评估重新检测模块中的候选对象，已进行了验证，这可以确保恢复的准确性。许多实验结果表明，我们提出的跟踪方法在准确性和鲁棒性方面优于最新方法。已经设计了一种切换策略，以动态激活重新检测模块并在本地搜索和全局搜索之间交互搜索模式。另外，我们的再检测器经过训练，可以围绕可靠的跟踪目标对正负样本进行采样，以适应外观变化。为了评估重新检测模块中的候选对象，已进行了验证，这可以确保恢复的准确性。许多实验结果表明，我们提出的跟踪方法在准确性和鲁棒性方面优于最新方法。已经设计了一种切换策略，以动态激活重新检测模块并在本地搜索和全局搜索之间交互搜索模式。另外，我们的再检测器经过训练，可以围绕可靠的跟踪目标对正负样本进行采样，以适应外观变化。为了评估重新检测模块中的候选对象，已进行了验证，这可以确保恢复的准确性。许多实验结果表明，我们提出的跟踪方法在准确性和鲁棒性方面优于最新方法。我们的再检测器经过训练，对可靠跟踪目标周围的正负样本进行采样以适应外观变化。为了评估重新检测模块中的候选对象，已进行了验证，这可以确保恢复的准确性。许多实验结果表明，我们提出的跟踪方法在准确性和鲁棒性方面优于最新方法。我们的再检测器经过训练，对可靠跟踪目标周围的正负样本进行采样以适应外观变化。为了评估重新检测模块中的候选对象，已进行了验证，这可以确保恢复的准确性。许多实验结果表明，我们提出的跟踪方法在准确性和鲁棒性方面优于最新方法。