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Mean-Shift Tracking Algorithm with Adaptive Kernel-Bandwidth of Target

机译:目标自适应核带宽的均值漂移跟踪算法

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The bandwidth of traditional Mean-Shift tracking algorithm can not be adapted to size change of target To overcome this problem, a new adaptive kernelbandwidth selection method' based on the comparison of Bhattacharyya coefficients is proposed in this paper. In this method, the Bhattacharyya coefficient is calculated by using histograms of target image and target background image, and then, a new Bhattacharyya coefficient is calculated according to the candidate image histogram of the current frame and the target background image during the process of tracking. Judging the changing trend of the target by comparing the two former coefficients and the kernelbandwidth is expanded or shrunk by 10% according to the judgment. Finally, the target area is specified after the calculation above. The simulation results show that the algorithm proposed in this paper verifies the effectiveness of tracking of the targets with changing scales.
机译:传统Mean-Shift跟踪算法的带宽不能适应目标的大小变化。为克服这一问题,提出了一种基于Bhattacharyya系数比较的自适应核带宽选择方法。该方法利用目标图像和目标背景图像的直方图来计算Bhattacharyya系数,然后根据当前帧和目标背景图像在跟踪过程中的候选图像直方图来计算新的Bhattacharyya系数。通过比较前两个系数来判断目标的变化趋势,并根据判断将内核带宽扩大或缩小10%。最后,在上述计算之后指定目标区域。仿真结果表明,本文提出的算法验证了尺度变化时目标跟踪的有效性。

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