Abstract Correlation Filter (CF) based algorithms play an important role in the field of Visual Object Tracking (VOT) due to their high accuracy and low computational complexity. While existing CF tracking algorithms suffer performance degradation due to inaccurate object modeling. In this paper, we improve the object modeling accuracy in both CF training stage and target detection procedure to preventing the drift problem. Specifically, we propose a multi-model structure for CF trackers to capture the target appearance changes, where different appearance models are trained with specific samples to catch the salient features of the target and reduce the computational cost. Furthermore, a space filter for detection features is designed to suppress the boundary effect under Gaussian motion prior, which contributes to improving the accuracy of position estimation. We deploy our method to three hand-crafted features based CF trackers to perform real-time visual tracking on popular benchmarks. The experimental results demonstrate the efficacy of our proposed scheme and the efficiency of our trackers. In addition, we provide a comprehensive analysis of the proposed method to facilitate application.

中文翻译：

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学习用于视觉跟踪的加权多模型相关滤波器

摘要 基于相关滤波器（CF）的算法由于其高精度和低计算复杂度在视觉对象跟踪（VOT）领域发挥着重要作用。虽然现有的 CF 跟踪算法由于对象建模不准确而导致性能下降。在本文中，我们提高了 CF 训练阶段和目标检测过程中的对象建模精度，以防止漂移问题。具体来说，我们为 CF 跟踪器提出了一种多模型结构来捕获目标外观变化，其中使用特定样本训练不同的外观模型以捕获目标的显着特征并降低计算成本。此外，设计了检测特征的空间滤波器来抑制高斯运动先验下的边界效应，这有助于提高位置估计的精度。我们将我们的方法部署到三个基于手工制作的特征的 CF 跟踪器，以在流行的基准测试上执行实时视觉跟踪。实验结果证明了我们提出的方案的有效性和我们的跟踪器的效率。此外，我们对所提出的方法进行了全面分析，以方便应用。