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Target tracking based on non-linear kernel density estimation and Kalman filter

机译：基于非线性核密度估计和卡尔曼滤波的目标跟踪

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摘要

This paper chooses Mean Shift algorithm to track target based on non-linear kernel density estimation and Kalman filter. Kernel density estimation is a probability density estimation method, which is used to detect moving target and update the target color histogram. The interest targets are obtained by labeling connected region in the detected binary image. Kalman filtering is employed to predict the position of the target being tracked, giving a starting searching window for Mean Shift tracking. Experimental results show that the method proposed is effective and fast in implementation, which satisfies the real-time requirement, it is capable of handling occlusion problem, meanwhile it is robust against the effects of unstable scene illumination.
机译：本文选择基于非线性核密度估计和卡尔曼滤波的均值漂移算法进行目标跟踪。核密度估计是一种概率密度估计方法，用于检测运动目标并更新目标颜色直方图。通过在检测到的二进制图像中标记连接区域来获得兴趣目标。卡尔曼滤波用于预测被跟踪目标的位置，从而为均值漂移跟踪提供起始搜索窗口。实验结果表明，该方法有效，快速，满足实时性要求，能够解决遮挡问题，同时对不稳定的场景照明效果也具有较强的鲁棒性。

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