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Adaptive kernel-bandwidth object tracking based on Mean-shift algorithm

机译:基于平均换档算法的自适应内核带宽对象跟踪

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摘要

With the wide application and development of Mean Shift algorithm, the shortages of the classical algorithm have been exposed. Among them, there is a defect that the kernel-bandwidth of the traditional object tracking algorithm based on Mean-Shift is fixed, which is very easy to cause the failure of the object tracking. To overcome this shortage, this paper puts forward an object tracking method that combines the canny operator with the Mean Shift algorithm. Firstly, this algorithm uses the canny edge detection to determine the change tendency of the object. Then proper ratio increment is used to adjust the Kernel-bandwidth of the Mean Shift. Finally, the object can be located accurately with appropriate kernel-bandwidth. The experimental results show that the improved algorithm can improves the tracking stability of the Mean Shift algorithm effectively when the size of the tracking object is changing and it is adaptive to the changing size of the tracking object.
机译:随着平均移位算法的广泛应用和开发,公开了经典算法的短缺。其中,存在基于平均偏移的传统对象跟踪算法的内核带宽是固定的,这很容易导致对象跟踪的故障。为了克服这一短缺,本文提出了一种对象跟踪方法,将Canny运算符与平均移位算法相结合。首先,该算法使用Canny Edge检测来确定对象的变化趋势。然后使用适当的比率增量来调整平均移位的内核带宽。最后,可以使用适当的内核带宽来准确地定位。实验结果表明,当跟踪物体的大小改变时,改进的算法有效地提高了平均移位算法的跟踪稳定性,并且它适应跟踪对象的变化尺寸。

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