一种AGMM配准的尺度自适应目标跟踪方法

Visual object tracking with the adaptive scale based on AGMM point sets matching

针对视觉目标跟踪中目标尺度发生变化时容易发生跟踪失败的问题,提出基于不对称高斯混合模型配准的尺度自适应目标跟踪方法.不对称高斯混合模型配准把上一帧和当前帧图像的特征点集分别作为高斯混合模型高斯重心和数据点,并将特征信息与空间信息相融合;通过比较数据点与高斯混合模型高斯重心之间的相似程度,对两帧图像之间的点集进行配准,得到当前帧中可靠的特征点;点集的离散程度充分反映了目标尺度大小,通过仿射变换计算图像离散度比例变化,可以准确地估计出当前帧目标框的位置和尺度.实验表明,该算法对目标尺度变化具有较强的自适应性,并且在发生光照变化、复杂背景时,也可以达到很好的效果.

A visual object tracking method with the adaptive scale based on AGMM（Asymmetrical Gauss Mixture Models）point sets matching is proposed aimed at adaptively following the object＇s scale changes,which often cause tracking failure.As the feature point set in the last frame is considered as the GMM centroids and the feature point set in the current frame represents the data respectively,AGMM fuses the feature information and spatial information;by comparing the similarity between data and GMM centroids,we match the point sets between two adjacent frames and obtain the reliable feature points in the current frame;the degree of dispersion between points in the point set accurately reflects the size of the object scale and by using affine transformation,the proportion of the two point sets is computed to estimate the position and scale of the bounding box in the current frame accurately and effectively.Experimental results demonstrate that the method is adaptive to scale change and has advantage in illumination variation and color similar target tracking.