(Discriminative) Correlation Filter has been successfully applied to visual tracking and has advanced the field significantly in recent years. Correlation filter-based trackers consider visual tracking as a problem of matching the feature template of the object and candidate regions in the detection sample, in which correlation filter provides the means to calculate the similarities. In contrast, convolution filter is usually used for blurring, sharpening, embossing, edge detection, etc in image processing. On the surface, correlation filter and convolution filter are usually used for different purposes. In this paper, however, we proves, for the first time, that correlation filter and convolution filter are equivalent in the sense that their minimum mean-square errors (MMSEs) in visual tracking are equal, under the condition that the optimal solutions exist and the ideal filter response is Gaussian and centrosymmetric. This result gives researchers the freedom to choose correlation or convolution in formulating their trackers. It also suggests that the explanation of the ideal response in terms of similarities is not essential.
翻译:近些年来,关系过滤器成功地应用于视觉跟踪,并大大推进了这个领域。 关系过滤器追踪器将视觉跟踪视为在探测样本中匹配对象和候选区域特征模板的问题, 其中相关过滤器提供了计算相似性的手段。 相反, 在图像处理中, 熔化过滤器通常用于模糊、 磨亮、 刺塞、 边缘检测等。 在表面, 关联过滤器和变异过滤器通常用于不同的目的 。 但是, 在本文中, 我们第一次证明, 相关性过滤器和变异过滤器是等同的, 也就是说, 在视觉跟踪中, 它们最小的平均值错误是相等的, 在最佳解决方案存在的情况下, 理想的过滤反应是高斯 和 摄氏度 。 这样, 研究人员就可以在绘制跟踪器时选择关联性或变异性。 这还表明, 在相似性方面对理想反应的解释并不必要 。