基于中心宏块的视频目标跟踪算法  

Tracking Video Object Based on Central Macroblocks

作　　者：肖国强[1] 康勤[1] 江健民[1] 张贝贝[1] 

机构地区：[1]西南大学计算机与信息科学学院,重庆400715

出　　处：《计算机学报》2011年第9期1712-1718,共7页Chinese Journal of Computers

基　　金：重庆市自然科学基金(CSTC-2008BB2252)资助~~

摘　　要：目前的视频目标跟踪算法对目标的不精确分割十分敏感,从而影响目标跟踪的性能.文中提出一种新的视频目标跟踪算法,该算法对目标的过分割或欠分割有较强的鲁棒性.文中提出的跟踪算法中引入了一个中心宏块的概念,通过两个层次的相似性度量,以建立相邻帧之间目标的对应关系.同时利用MPEG的运动估计技术和Kalman滤波技术来提高目标跟踪的性能.第一个层次的相似性度量是通过SAD值在中心宏块之间进行局部纹理匹配;第二个层次是用描述目标内部结构的方向矢量建立目标间的对应关系.实验结果表明,文中提出的算法对于不精确分割的目标能够成功地进行跟踪,同时,对于目标的遮挡、形变、出现、消失以及光线的影响有较强的鲁棒性.Since the approaches suggested so far for video moving object tracking are sensitive to the accuracy of object segmentation, we propose a new video object tracking algorithm that provides the strength of robustness to the problems of both under-segmentation and over-segmentation. The proposed tracking algorithm introduces a concept of central macroblock, which is used to establish the correspondences between objects inside neighboring frames via two levels of similarity measurement and observations. Furthermore, MPEG motion estimation and compensation and Kalman filtering techniques are also exploited to enhance the tracking performances. While the first level of similarity measurements is limited to local texture matching via SAD （Sum of Absolute Differences） values between central macroblocks, the second level is established upon objects via directional vectors, characterizing the internal structure of the segmented ohjects. And both levels of similarity measurements are integrated by Kalman filtering. Experimental results carried out on PETS2001 and PETS2006 database show that the proposed algorithm achieves successful tracking performances robust to inaccuracy of object segmentation as well as other distracting factors such as occlusion, deformation, lighting effect, object disappearances and appearances etc.

关 键 词：目标跟踪 视频处理 视频分割 KALMAN滤波 中心宏块 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]