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Long-term Scale Adaptive Tracking with Kernel Correlation Filters

机译：核相关滤波器的长期尺度自适应跟踪

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Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.
机译：视频序列中的目标跟踪在军事和民用领域都有广泛的应用。但是，随着输入视频序列长度的增加，会出现许多问题，例如严重的物体遮挡，物体外观变化和物体视线（某些部分或整个物体离开图像空间）。为了解决这些问题并从混乱的背景中识别出要跟踪的对象，我们提供了一个稳健的外观模型，使用了快速鲁棒特征（SURF）和先进的集成特征，包括Felzenszwalb的定向梯度直方图（FHOG）和颜色属性。由于重新检测对于长期跟踪至关重要，因此我们基于移动区域检测开发了一种有效的对象重新检测策略。我们在算法设计中采用了流行的内核相关滤波器，这有助于高速对象跟踪。我们使用CVPR2013对象跟踪基准（OTB2013）数据集进行的评估表明，在各种挑战性场景中，该算法的性能优于参考最新技术的跟踪器。

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