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Mean-Shift tracking algorithm with adaptive kernel-bandwidth of target

机译:具有目标自适应内核 - 带宽的平均移位跟踪算法

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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 kernel-bandwidth 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 kernel-bandwidth 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:传统平均换档跟踪算法的带宽不能适应目标的大小变化。为了克服这个问题,本文提出了一种基于Bhattacharya系数比较的新的自适应核 - 带宽选择方法。在该方法中,通过使用目标图像和目标背景图像的直方图计算Bhattacharya系数,然后,根据当前帧的候选图像直方图和在跟踪过程中的目标背景图像的候选图像直方图计算新的Bhattacharya系数。通过比较两个以前系数和内核带宽来判断目标的变化趋势,并根据判断将核心带宽扩展或缩小10%。最后,在上面计算后指定目标区域。仿真结果表明,本文提出的算法验证了随着变化的尺度跟踪目标的有效性。

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