[1]梅雪,胡石,许松松,等.基于多尺度特征的双层隐马尔可夫模型及其在行为识别中的应用[J].智能系统学报,2012,7(6):512-517.
　MEI Xue,HU Shi,XU Songsong,et al.Multi scale feature based double layer HMM and its application in behavior recognition[J].CAAI Transactions on Intelligent Systems,2012,7(6):512-517.
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基于多尺度特征的双层隐马尔可夫模型及其在行为识别中的应用

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 7 期数: 2012年第6期 页码: 512-517 栏目: 学术论文—机器感知与模式识别 出版日期: 2012-12-25
Title:
Multi scale feature based double layer HMM and its application in behavior recognition
文章编号:
1673-4785(2012)06-0512-06
作者:
梅雪，胡石，许松松，张继法
南京工业大学 自动化与电气工程学院，江苏 南京 211816
Author(s):
MEI Xue, HU Shi, XU Songsong， ZHANG Jifa
College of Automation and Electrical Engineering, Nanjing University of Technology, Nanjing 211816, China
关键词:
双层隐马尔可夫模型; 行为识别; 多尺度特征; 智能视频监控
Keywords:
doublelayer HMM (DLHMM);  behavior recognition;  multiscale feature;  intelligent video surveillance
分类号:
TP391.4
文献标志码:
A
摘要:
借鉴人类视觉感知所具有的多尺度、多分辨性的特性，针对智能视频监控系统的人体运动行为识别，提出了一种基于多尺度特征的双层隐马尔可夫模型.根据人体行为关键姿态数确定HMM的状态数目，发掘人体运动行为隐藏的多尺度结构间的关系，将运动轨迹和人体姿态边缘小波矩2个不同尺度特征应用于2层HMM，提供更为丰富的行为尺度间的相关信息.分别用Weizmann人体行为数据库和自行拍摄的室内视频，对人体运动行为识别进行仿真实验，结果表明，五状态HMM模型更符合人体运动行为特点，基于多尺度特征的五状态双层隐马尔可夫模型具有较高的识别率.
Abstract:
Learning from multiscale and multidistinguish attributes of human beings’ visual perception and aiming at human movement behavior recognition in intelligent video surveillance system, a doublelayer hidden markov model (DLHMM) is developed based on multiscale behavior features. Considering the human behavior characteristics, the number of HMM states is according to the number of key gestures selected. Discovering the relationship between the multiscale structures hidden in the human movement behavior, two different scale featureshuman motion trajectory and wavelet moment of human gesture’s edge, are applied respectively in two layers of DLHMM, so as to provide more scale information about behavior. Experiments, using Israel Weizmann human behavior database and human actions indoor recorded by ourselves, show the fivestate HMM more accords with the human motion behavior characteristics, and the fivestate DLHMM based on multiscale feature has a higher recognition rate compared with traditional methods using one layer HMM.
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备注/Memo

收稿日期： 2012-03-15.
网络出版日期：2012-11-16.
基金项目：江苏省高校自然科学基金资助项目（09KJB510002）; 江苏省博士后科研资助计划资助项目（1001027B）;南京工业大学青年学科基金资助项目（39710006）.
通信作者：梅雪.
E-mail: mx@njut.edu.cn.
作者简介：
梅雪,女，1975年生，副教授，硕士生导师，主要研究方向为图像处理、模式识别及计算机视觉.
胡石，男，1988年生，硕士研究生，主要研究领域为模式识别、图像处理.
张继法，男，1987年生，硕士研究生，主要研究领域为模式识别、图像处理.

更新日期/Last Update: 2013-03-19
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