In the part of tracking, combining the improved Mean-Shift algorithm and Kalman filter prediction.
在跟踪部分，采用改进的均值漂移算法和卡尔曼滤波预测结合。
Finally we use this dynamic model of optimization in mean-shift algorithm, and compute object's true position.
最后将最优化的运动模型用于基于核的均值转移算法中，从而获得运动目标的精确位置。
The Gaussian kernel mean-shift algorithm which is deduced from kernel density estimation has not been widely employed in applications because of its low convergence rate.
由核密度估计推导获得的高斯核均值漂移算法因收敛速度慢在应用中效率不高。
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