Sampling Operator to Learn the Scalable Correlation Filter for Visual Tracking
Sampling Operator to Learn the Scalable Correlation Filter for Visual Tracking
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DOI：
10.1109/access.2019.2892429
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Minkyu Lee;Taeoh Kim;Yuseok Ban;Eungyeol Song;Sangyoun Lee
中科院分区：
文献类型：
--
作者：
Minkyu Lee;Taeoh Kim;Yuseok Ban;Eungyeol Song;Sangyoun Lee
相关滤波器由于其低计算复杂度和良好的性能而适用于跟踪。然而，可用训练样本的数量受到滤波器大小的限制，样本的缺乏会导致泛化能力较差。此外，光谱泄漏会降低滤波器的质量。因此，在本文中，我们提出了一种采样算子，用于在放大窗口中学习可扩展相关滤波器，其尺寸大于对象尺寸。可伸缩滤波器对稀疏频率表示进行编码，以重建一个更大的滤波器，其零点位于空间域中的对象之外。采样算子由加窗和采样操作组成，可以从大窗口学习可扩展滤波器，并抑制频谱泄漏。我们的方法在 OTB-100、TC-128 和 UAV-123 数据集上进行了评估，并在精度和成功率方面取得了有希望的结果。
The correlation filter is suitable for tracking on account of its low computational complexity and promising performance. However, the number of available training samples is limited to the filter size, and the lack of samples leads to poor generalization. Moreover, spectral leakage degrades the filter quality. In this paper, we, therefore, propose a sampling operator for learning a scalable correlation filter in an enlarged window, whose size is larger than the object size. The scalable filter encodes the sparse frequency representation to reconstruct a larger filter with zeros outside of the object in the spatial domain. The sampling operator, which is composed of windowing and sampling operations, enables learning the scalable filter from a large window, and it suppresses spectral leakage. Our method was evaluated on the OTB-100, TC-128, and UAV-123 datasets and achieved the promising results in terms of precision and success rates.