This paper investigates long-term visual object tracking which is a complex problem in computer vision community and big data analysis, due to the variation of the target and the surrounding environment. A novel tracking algorithm based on local correlation filtering and global keypoint matching is proposed to solve problems occurred during long-term tracking such as occlusion, target-losing, etc. The algorithm consists of two major components: (1) local object tracking module, and (2) global losing re-detection module. The local tracking module optimizes the conventional correlation filtering algorithm. Firstly, the Color Name feature is applied to increase the color sensitivity. Secondly, a scale traversal is employed to accommodate target scale changes. In the global losing re-detection module, the target losing judgment and global re-detection is realized by keypoint feature models of foreground and background. The proposed tracker achieves the 1st place in the VTB50 test set with 81.3% precision and 61.3% success rate, which outperforms other existing state-of-the-art trackers by over 10%. And it achieves the 2nd place in our Chasing-Car test set with a higher real-time performance 43.2 fps. The experimental results show that the proposed tracker has higher accuracy and robustness when dealing with situations like object deformation, occlusion and target-losing, etc.

中文翻译：

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基于多尺度局部相关滤波和全局重检测的长期实时目标跟踪

本文研究了长期视觉对象跟踪，这是计算机视觉社区和大数据分析中的一个复杂问题，由于目标和周围环境的变化。针对长时间跟踪中出现的遮挡、目标丢失等问题，提出了一种基于局部相关滤波和全局关键点匹配的新型跟踪算法。该算法由两个主要部分组成：（1）局部目标跟踪模块， (2) 全局丢失重检测模块。局部跟踪模块优化了传统的相关滤波算法。首先，应用颜色名称功能来增加颜色敏感度。其次，采用尺度遍历来适应目标尺度的变化。在全局丢失重新检测模块中，通过前景和背景的关键点特征模型实现目标丢失判断和全局重新检测。所提出的跟踪器在 VTB50 测试集中以 81.3% 的精度和 61.3% 的成功率获得第一名，比其他现有的最先进的跟踪器高出 10% 以上。它在我们的 Chasing-Car 测试集中以更高的实时性能 43.2 fps 获得第二名。实验结果表明，所提出的跟踪器在处理物体变形、遮挡和目标丢失等情况时具有更高的精度和鲁棒性。它在我们的 Chasing-Car 测试集中以更高的实时性能 43.2 fps 获得第二名。实验结果表明，所提出的跟踪器在处理物体变形、遮挡和目标丢失等情况时具有更高的精度和鲁棒性。它在我们的 Chasing-Car 测试集中以更高的实时性能 43.2 fps 获得第二名。实验结果表明，所提出的跟踪器在处理物体变形、遮挡和目标丢失等情况时具有更高的精度和鲁棒性。