Real time occlusion handling using Kalman Filter and mean-shift

R. Panahi, I. Gholampour, M. Jamzad
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引用次数: 7

Abstract

Tracking objects using Mean Shift algorithm fails when there is a full/partial occlusion or when the background color and the desired object are close. In this paper we proposed a method using Kalman Filter and Mean Shift for handling these situations. Using similarity measure of Mean Shift algorithm we are able to detect an occlusion. Kalman Filter comes into the play for occlusion handling in a Buffer-Mode Process. We implemented this algorithm both on PC and DSP 64x+ Texas Instrument and the results are both tabulated. The results reveal the ability of our method to locate the object soon after occlusion disappearance.
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使用卡尔曼滤波和均值移位的实时遮挡处理
当存在完全/部分遮挡或背景颜色与期望对象接近时,使用Mean Shift算法跟踪对象失败。本文提出了一种利用卡尔曼滤波和均值移位来处理这些情况的方法。利用Mean Shift算法的相似性度量来检测遮挡。卡尔曼滤波在缓冲模式过程中用于遮挡处理。我们在PC和DSP 64x+德州仪器上分别实现了该算法,并将结果制成表格。结果表明,我们的方法能够在遮挡消失后快速定位目标。
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