基于四通道不可分小波的均值漂移目标跟踪方法  

Mean shift object tracking method based on four channel non-separable wavelets

作　　者：刘斌 郑凯凯 

机构地区：[1]湖北大学计算机与信息工程学院,湖北武汉430062

出　　处：《量子电子学报》2018年第1期13-22,共10页Chinese Journal of Quantum Electronics

基　　金：国家自然科学基金;61471160~~

摘　　要：针对均值漂移跟踪算法中目标模型更新误差累计导致的后续跟踪误差变大,提出了一种基于四通道不可分小波的均值漂移目标跟踪方法。基于不可分小波对目标图像的分解,利用高频子图分割出准确的目标区域,将此区域的高频与低频特征值融合,进行均值漂移跟踪。在跟踪过程中使用基于目标轮廓的尺度与模型更新,并用子特征相关系数对目标特征模型进行自适应更新。结果表明提出的方法在跟踪场景和目标外观变化时具有实时性与准确性。与未进行图像分割的跟踪方法相比,提出的方法具有更好的跟踪精准度;与使用条件随机场(CRF)的跟踪方法相比,其具有更好的处理速度与精确性。In order to solve the problem that the cumulative error caused by model updating on mean shift object tracking becomes larger in the following tracking, a mean shift object tracking method based on four channel non-separable wavelets is presented. Based on the decomposition of target image by non-separable wavelets, the accurate target region is segmented by using high frequency sub-images. The high and low frequency characteristic values of this region are fused, and mean shift tracking is carried out. During tracking, the scale and model updating based on the target contour are used, and the adaptive updating of target feature model is carried out by using correlation coefficient of the sub-features. Results show that the proposed method has both real-time ability and accuracy in tracking the change of scene and target shape. Compared with the tracking methods without image segmentation, the proposed method has better tracking accuracy. Compared with the tracking method using conditional random field （CRF）, it has better processing speed and accuracy.

关 键 词：图像处理 计算机视觉 目标跟踪 不可分小波 图像分割 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]