基于核密度估计尺度空间的目标跟踪算法

Object tracking using the trust-region method in scale-space with probability distribution images generated by kernel density estimation

为能够准确表示目标颜色分布、适应目标尺寸连续变化,提出了一种新的序列图像目标跟踪算法。该算法首先计算目标区域颜色概率分布的核密度估计函数,然后通过规整化每一帧输入图像像素在此函数上的取值生成目标概率分布图。最后通过检测多尺度规范化Laplacian滤波的极值,实现目标的定位和尺寸描述。与基于直方图的算法比较并结合大量真实序列图像上的实验验证表明,该算法更好地描述了目标颜色特征,提高了跟踪算法的精度。

In the widely used mean shift-based tracking algorithms, targets are described by color histograms with their size determined using predefined parameters. However histograms affect the precision of the target＇s color description and it＇s impossible to precisely describe a target＇s size with discrete parameters. This paper presents a new approach for tracking objects in image sequences which precisely tracks the constant changes of the target＇s size. A reference image gives the kernel density estimate （KDE） of the target＇s color distribution. Then for each incoming frame, a probability distribution image of the target is created through evaluating and normalizing of the KDE. Through searching the local maxima of multi-scale normalized Laplacian filters of the probability distribution image the target is located and its size is determined. Comparison with histogram-based algorithms show that the new method describes the target more accurately and thus achieves much better tracking precision.