基于Mean Shift的变尺度快速运动目标自适应跟踪算法

为了实现对变尺度快速运动目标的良好跟踪,在对传统Mean Shift跟踪算法改进的基础上,提出了一种运动目标自适应跟踪算法。该算法首先采用目标区域的像素点空域加权后的彩色图像作为初始帧目标模板,目标的真实位置利用Mean Shift算法迭代求得,从而实现对快速运动目标的空间定位,然后将相邻帧的目标采用尺度不变特征变换(SIFT)算子进行特征匹配,根据目标的缩放因子实时更新下一帧的核带宽,修正算法跟踪窗口的尺寸,以适应目标尺度的变化,从而实现对快速运动目标的尺度定位。最后,通过实验表明,与传统的Mean Shift跟踪算法相比,该算法的跟踪准确率达到97%以上,能够实现对变尺度快速运动目标的精确跟踪。

Auto-adaptive tracking algorithm for fast moving target with variable scale based on Mean Shift

An auto-adaptive tracking algorithm for fast moving target is put forward based on the improved traditional Mean Shift tracking algorithm, in order to achieve good tracking of fast moving target with variable scale. This algorithm firstly adopts the color image constituted by the pixels of target region with spatial weighting as initial frame object template, and the true position of target is obtained by the iteration of Mean Shift algorithm, therefore the spatial localization of fast moving target is realized. Then the features of adjacent frame targets are matched by Scale Invariant Feature Transform(SIFT) operator; the kernel bandwidth of next frame is updated in real time according to the scaling factor of target; the tracking window size of the algorithm is amended, which can adapt to the variable scales of the target, so the scale localization of fast moving target is achieved. Finally, the experiments demonstrate that compared with the traditional Mean Shift tracking algorithm, the tracking accuracy rate of the algorithm is above 97%, and the algorithm can accurately track the fast moving target with variable scales.