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An improved spatio-temporal context tracking algorithm based on scale correlation filter:

机译:一种改进的基于尺度相关滤波器的时空上下文跟踪算法:

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Spatio-temporal context algorithm is commonly used in geology and has been introduced into the field of target tracking in recent years. The algorithm improved the robustness of visual tracking through dense contextual information around the target and thus achieved great tracking results. However, updating errors may occur in spatio-temporal context algorithm when the target has rapid changes in scale and appearance, resulting in the algorithm cannot extract the target area accurately and completely. In order to overcome this problem, this article proposes an improved spatio-temporal context algorithm based on scale correlation filter. First of all, the algorithm extracts samples of different scales around the target after the target is settled by spatio-temporal context algorithm and then forms the pyramid of scale characteristics through samples extracted by histogram of oriented gradients operator. Second, the best scale parameter will be achieved by means of scale correlation filter to update the sca...
机译:时空上下文算法是地质学中常用的方法,近年来已被引入到目标跟踪领域。该算法通过围绕目标的密集上下文信息提高了视觉跟踪的鲁棒性,因此获得了不错的跟踪结果。然而,当目标的尺度和外观发生快速变化时,时空上下文算法中可能会出现更新错误,导致算法无法准确,完整地提取目标区域。为了克服这个问题,本文提出了一种基于尺度相关滤波器的改进的时空上下文算法。首先,该算法通过时空上下文算法确定目标后,提取目标周围不同尺度的样本,然后通过定向梯度直方图提取的样本形成尺度特征的金字塔。其次,通过比例相关滤波器来更新比例尺,可以获得最佳比例参数。

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