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A two-stage object tracking method based on Curvelet transform and mean shift algorithm

机译:基于Curvelet变换和均值漂移算法的两阶段目标跟踪方法

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Traditional mean shift tracking algorithm couldn't track moving objects in cross-scale domain. In this paper, we propose a new two-stage object tracking method combined Curvelet Transform and mean shift algorithm. Our proposed method extracts image features using Curvelet transform, and calculates object location by cross-scale mean shift algorithm. The experimental results demonstrate that the proposed algorithm can effectively track moving objects. Compared with traditional mean shift algorithm, tracking accuracy has been significantly improved.
机译:传统的均值漂移跟踪算法无法跨尺度域跟踪运动对象。本文提出了一种结合Curvelet变换和均值平移算法的两阶段目标跟踪方法。我们提出的方法使用Curvelet变换提取图像特征,并通过跨尺度均值漂移算法计算对象位置。实验结果表明,该算法可以有效地跟踪运动物体。与传统的均值漂移算法相比,跟踪精度有了明显提高。

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