基于隐马尔可夫模型的多摄像头人体对象的目标识别  

Multi-camera person identification based on hidden markov model

作　　者：高鹏[1] 郭立君[1] 朱一卫 张荣[1] 

机构地区：[1]宁波大学信息科学与工程学院,浙江宁波315211 [2]国家电网浙江省电力公司宁波供电公司,浙江宁波315099

出　　处：《计算机应用》2014年第6期1746-1752,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61175026);浙江省新一代移动互联网用户端软件科技创新团队项目(2010R50009);宁波大学胡岚博士基金资助项目(ZX2013000319);宁波大学人才工程项目(20111537)

摘　　要：在非重叠多摄像机系统的人体对象目标识别中,针对基于单幅图片的识别算法不能较好处理对象表观和视角变化的问题,提出基于人体图像序列的算法。该算法用隐马尔可夫模型(HMM)融合多幅图片的特征,先考虑人体结构的约束,将人体图像在垂直方向上划分为多个相等的图像区域;然后采用多层阈值分割算法提取区域代表性颜色特征(SRC)和标准差特征(SSV);再用每个人体对象的多幅图片提取的特征数据集训练该对象的连续密度HMM;最后利用训练的模型实现人体对象的目标识别。该方法在两个公开数据集上进行的实验都获得了较高的识别率,提高了对摄像头视角变化、低分辨率的鲁棒性,且简单易实现。In the non-overlapping filed of muhi-camera system, the single-shot person identification methods cannot well deal with appearance and viewpoint changes. Based on the multiple frames acquired from surveillance cameras, a new technique which combined Hidden Markov Model （HMM） with appearance-based feature was proposed. First, considering the structural constraint of human body, the whole：body appearance of each individual was equally vertically divided into sub- images. Then multi-level threshold method was used to extract Segment Representative Color （SRC） and Segment Standard Variation （SSV） feature. The feature dataset acquired from multiple frames was applied to train continuous density HMM, and the final recognition was realized by these well-trained model. Extensive experiments on two public datasets show that the proposed method achieves high recognition rate, improves robustness against viewpoint changes and low resolution, and it is simple and easy to realize.

关 键 词：人体对象目标识别 隐马尔可夫模型 多阈值分割 图像序列 峰值信噪比 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP37[自动化与计算机技术—计算机科学与技术]