基于SIFT特征和模板更新的粒子滤波目标跟踪算法  

PARTICLE FILTER OBJECT TRACKING ALGORITHM BASED ON SIFT FEATURES AND MODEL UPDATE

作　　者：曹正洁 王汇源[1] 张元元[2] 江二华 

机构地区：[1]山东大学信息科学与工程学院,山东济南250100 [2]山东省科学院情报研究所,山东济南250000

出　　处：《计算机应用与软件》2014年第12期298-302,共5页Computer Applications and Software

摘　　要：传统的粒子滤波算法利用目标的颜色特征对粒子权值进行更新,当背景与目标的颜色分布相似或者目标被遮挡时,易发生误跟踪。尺度不变特征具有较高的独特性,但是仅使用SIFT特征不足以对小目标进行描述。针对这两种情况,提出一种利用SIFT特征和颜色特征建立目标模型,结合粒子滤波实现目标跟踪的新方法。为了防止目标模板的误更新,根据当前帧跟踪结果与SIFT目标模板中特征点的匹配数目决定是否对颜色目标模板进行更新。实验结果表明,当目标被遮挡或者位于有相似颜色的杂乱背景时,提出的方法能有效提高跟踪的精确度。In traditional particle filter algorithm, the weight of each particle is updated only by the colour feature of the object, which may easily lead to error tracking when the object and the background have similar colour distribution or the object is occluded.Scale-invariant features are highly discriminative, but to use SIFT only is insufficient to describe small targets.A new method is proposed in this paper to handle with these two situations, in which the target model is built by SIFT feature and colour feature, and the particle filter is integrated to achieve object tracking.In order to avoid error updating of the target model, in this paper whether the colour target model is to be updated or not depends on the number of matching feature points between the tracking result in current frame and the SIFT target model.Experimental results show that the proposed method can effectively improve the tracking precision especially when the object is occluded or under clutter background with similar colours.

关 键 词：目标跟踪 粒子滤波 尺度不变特征 模板更新 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]