一种基于多特征聚类的粒子滤波跟踪算法  

A Particle Filter Tracking Algorithm Based on Multi-Feature Clustering

作　　者：包加桐[1] 郭晏[1] 唐鸿儒[2] 宋爱国[1] 

机构地区：[1]东南大学仪器科学与工程学院,江苏南京210096 [2]扬州大学能源与动力工程学院,江苏扬州225127

出　　处：《机器人》2011年第5期634-640,共7页Robot

基　　金：国家863计划资助项目(2006AA04Z246);国家自然科学基金资助项目(61075068);教育部重大创新工程培育资金项目(708045);江苏省自然科学基金重点项目(BK2009183);江苏省自然科学基金重点项目(BK2010063)

摘　　要：提出了一种基于多特征聚类的粒子滤波目标跟踪算法.针对目标描述特征的多样性、特征分布描述方法的差异性及特征空间结构的任意性,提出将目标模型多特征表示统一在聚类计算框架下.算法利用基于均值移动的特征空间分析方法来自适应地计算任意结构特征空间中的聚类,在聚类的基础上提出了一种高效准确的目标概率密度估计方法来表示目标模型.利用核密度估计相似度量方法计算参考目标与候选目标的距离,作为粒子滤波系统观测的重要信息.提出了改进的粒子传播模型,有效提高粒子利用率.在大量真实序列图像上,使用LUV颜色特征与LBP纹理特征进行了目标跟踪实验.实验结果表明,提出的算法能获得较高的跟踪精度、鲁棒性强且满足实时性要求,与一些其它典型的算法相比,整体跟踪性能更好.A particle filter tracking algorithm based on multi-feature clustering is proposed.To address the issues such as the diversity of target features,the difference between methods of feature distribution description,and the arbitrariness of feature spatial structure,the multi-features representation of target model is unified into a clustering computing framework. The mean shift based feature space analysis approach is employed to adaptively calculate the clusters in any arbitrarily structured feature space.Based on the clusters,a target probability density estimation method,which is efficient and accurate, is proposed to represent the target model.The distance between the reference target and the candidate is calculated by the similarity measure of kernel density estimation,and is taken as important information for observation in particle filter system. To efficiently enhance the utilization rate of particles,an improved particle propagation model is presented.The object tracking experiments are performed on many real image sequences by using the LUV color features and the LBP（local binary pattern） texture features.Experiment results show that the proposed algorithm can obtain high tracking accuracy and strong robustness,meet real-time demand,and provide better tracking performance comparing with other typical algorithms.

关 键 词：目标跟踪 聚类分析 概率密度估计 粒子滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]