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A Hybrid Tracking Method Based on Active Contour and Mean Shift Algorithm

机译:基于主动轮廓和均值漂移算法的混合跟踪方法

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摘要

Active Contour is a very accurate in target tracking and robust to variety of illumination, translation, rotation and large scale. Unfortunately, the problem that Active Contour method can not track target in real time because of huge computation is still not well resolved. On the contrast, Mean Shift is a very fast algorithm in target tracking but sensitive to the change of illumination, and canȁ9;t get the desription of targetȁ9;s shape. In our work, we use Mean Shift method to determine the translation motion of target and build a initial rough contour for Active Contour method. So the computation of Active Contour method in early curves evolution stage is greatly reduced and the target can be tracked accurately by minimizing the energy function in few number of interative computation. Experimental results validate our method.
机译:Active Contour在目标跟踪方面非常准确,并且对各种照明,平移,旋转和大规模都具有鲁棒性。不幸的是,Active Contour方法由于计算量大而无法实时跟踪目标的问题仍未得到很好的解决。相比之下,Mean Shift是目标跟踪中非常快速的算法,但对光照的变化很敏感,并且无法获得目标9形状的描述。在我们的工作中,我们使用均值平移法来确定目标的平移运动,并为主动轮廓法建立初始的粗糙轮廓。因此,主动轮廓法在早期曲线演化阶段的计算量大大减少,并且通过最少的迭代计算,通过最小化能量函数,可以精确地跟踪目标。实验结果验证了我们的方法。

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