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Robust object tracking based on ridge regression and multi-scale local sparse coding

机译：基于岭回归和多尺度局部稀疏编码的鲁棒目标跟踪

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摘要

Recently, the technology of visual object tracking has achieved great success. However, it is still extraordinary challenging for some factors, such as scale variations, partial occlusions and so on. To deal with the problem of scale variations of the target, this paper proposes a hybrid tracking algorithm based on ridge regression and multi-scale local sparse coding. The hybrid tracking algorithm contains three parts. Firstly, a discriminative model based on two ridge regression models which include a correlation filtering ridge regression model and a color statistics ridge regression model, is used to estimate the approximate position of the target. Secondly, a multi-scale local sparse coding with particle filtering model, which combines local overlapped patches and local non-overlapped patches, is used to estimate the precise position and scale variations of the target. Thirdly, the appearance model of the target in the discriminative model based on ridge regression is updated according to the precise position and scale variations of the target in the second part. At the end, extensive experiments verify the effectiveness of the hybrid tracking algorithm in dealing with scale variations of the target.
机译：近年来，视觉目标跟踪技术取得了巨大的成功。但是，对于某些因素（例如比例变化，部分遮挡等）仍然是非凡的挑战。针对目标尺度变化的问题，提出了一种基于岭回归和多尺度局部稀疏编码的混合跟踪算法。混合跟踪算法包含三个部分。首先，基于包括相关滤波脊回归模型和颜色统计脊回归模型的两个脊回归模型的判别模型用于估计目标的近似位置。其次，结合局部重叠小块和局部非重叠小块的带有粒子滤波模型的多尺度局部稀疏编码被用于估计目标的精确位置和尺度变化。第三，根据第二部分中目标的精确位置和比例变化，更新基于岭回归的判别模型中目标的外观模型。最后，大量实验验证了混合跟踪算法在处理目标规模变化方面的有效性。

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