特征自适应的精准目标跟踪算法

提出了一种能够自动选择最优特征、精准描述目标尺寸连续变化的新的序列图像目标跟踪算法.该算法首先计算由RGB分量线性组合而成的区分目标和背景的最优特征,将每一帧榆入图像按照此最优特征转换成目标概率分布图,然后通过检测该图在尺度空间中微分滤波器输出的极值,来决定目标的尺度.最后采用QP_TR信任城算法在尺度空间里和图像平面内快速搜索概率分布图多尺度规范化Laplacian滤波函数的极值,实现了目标的定位,从而完成了跟踪任务.通过与现有算法进行比较,并结合大量真实序列图像进行实验验证,结果表明算法不但能够更准确地描述目标的大小,而且显著提高了跟踪算法的精度.

New feature adaptive high precision video tracking algorithm

A new approach of tracking objects in image sequences is proposed,in which the constant changes of the target's size can be precisely described.For each incoming frame,a probability distribution image of the target is created according to the best automatically chosen feature which is a linear combination of RGB components.In the generated probability distribution image the target's area turns into a blob.The scale of this blob can be determined based on the local maxima of differential scale-space filters.The QP_TR trust region algorithm is employed to search for the local maxima of multi-scale normalized Laplacian filter of the probability distribution image to locate the target as well as determine its scale.In the presented tracking examples,the new method is capable of describing the target more accurately and thus achieves much better tracking precision.