Abstract This paper presents a novel correlation filter-based tracking method for robust visual object tracking in the presence of partial occlusion, large-scale variation and model drift. To do this, first, we develop a correlation filter for predicting the target location based on the distribution of correlation response. In this formulation, the correlation response of the target image follows Gaussian distribution to estimate the target location efficiently. Second, the constraints are derived using kernel ridge regression to mitigate the target failure in object tracking. Third, we propose an adaptive scale estimation method to detect the target scale changes during the tracking. In addition, two feature integration is elaborately designed to improve the discriminative strength of the correlation filter. Finally, extensive experimental results on OTB2013, OTB2015, TempleColor128 and UAV123 datasets demonstrate that the proposed method performs favourably against several state-of-the-art methods.

中文翻译：

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用于鲁棒视觉对象跟踪的高斯响应相关滤波器

摘要 本文提出了一种新的基于相关滤波器的跟踪方法，用于在存在部分遮挡、大尺度变化和模型漂移的情况下进行鲁棒的视觉对象跟踪。为此，首先，我们开发了一个相关滤波器，用于根据相关响应的分布预测目标位置。在这个公式中，目标图像的相关响应遵循高斯分布以有效估计目标位置。其次，约束是使用内核岭回归导出的，以减轻对象跟踪中的目标失败。第三，我们提出了一种自适应尺度估计方法来检测跟踪过程中的目标尺度变化。此外，精心设计了两个特征集成，以提高相关滤波器的判别力。最后，