[1]田国会,吉艳青,李晓磊.家庭智能空间下基于场景的人的行为理解[J].智能系统学报,2010,5(01):57-62.
 TIAN Guo-hui,JI Yan-qing,LI Xiao-lei.Human behaviors understanding based on scene knowledge in home intelligent space[J].CAAI Transactions on Intelligent Systems,2010,5(01):57-62.
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家庭智能空间下基于场景的人的行为理解(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第5卷
期数:
2010年01期
页码:
57-62
栏目:
出版日期:
2010-02-25

文章信息/Info

Title:
Human behaviors understanding based on scene knowledge in home intelligent space
文章编号:
1673-4785(2010)01-0057-06
作者:
田国会吉艳青李晓磊
山东大学 控制科学与工程学院,山东 济南 250061
Author(s):
TIAN Guo-hui JI Yan-qing LI Xiao-lei
School of Control Science and Engineering, Shandong University, Ji’nan 250061, China
关键词:
智能空间机器视觉场景信息行为理解异常检测意图识别
Keywords:
intelligent space machine vision scene knowledge behavioral understanding anomaly detection intention recognition
分类号:
TP391
文献标志码:
A
摘要:
为了更好地在日常生活中给人提供智能化服务,对家庭环境下人的行为理解问题进行了研究.首先利用运动目标检测方法提取运动人体在环境中的坐标,然后结合行为特点把场景划分成不同区域,建立人体在环境中的位置关联矩阵和时空关联矩阵.通过马尔可夫模型统计出人体在空间中的位置状态转移概率矩阵及其状态持续时间矩阵,生成日常行为模板.根据当前行为与日常行为模板的相似度可检测出反常习惯和突发异常行为,同时可根据不同区域的行为模式分析人的意图.在智能空间平台下利用机器视觉技术基于场景信息实现了人的行为理解,并通过实验表明了方法的有效性.
Abstract:
Challenges in understanding human behavior in a home environment were studied in order to provide more intelligent services. First, spatial coordinates of human bodies were extracted from motion detector data. Then, by dividing the environment into different stations and observing the types of behavior typical at various periods of time in that area, a stationbased occupational matrix with a time dimension was established. After establishing the station state transitional probability matrix and the state duration time distribution matrix based on the Markov model, a daily behavioral template was constructed. Behavior outside of normal habits as well as behavior resulting from unexpected accidents could be detected in realtime by comparing the similarity of current behavior with templates showing typical daily behavior. At the same time, human intention could be predicted based on behavior patterns typical in different areas. In this way, better understanding of human behavior becomes possible. The effectiveness of this method was proved by experiments. 

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2008-11-22.
基金项目:国家高技术研究发展计划重点资助项目(2006AA040206).
通信作者:田国会.E-mail:g.h.tian@sdu.edu.cn.
作者简介:
田国会,男,1969年生,教授、博士生导师、工学博士,山东大学控制科学与工程学院副院长,中国人工智能学会理事.主要研究方向为服务机器人、智能空间、多机器人系统的协调与协作等.作为课题负责人或主要参加人员已完成包括国家自然科学基金项目、国家863计划项目、国防预研项目、中国博士后科学基金项目、山东省自然科学基金项目等15项.获山东省科技进步二等奖、山东省教委科技进步(自然科学理论)一等奖各1项.发表学术论文110余篇.
 吉艳青,女,1984年生,硕士研究生.主要研究方向为服务机器人、智能空间、基于视觉的监控和行为理解.
 李晓磊,男,1973年生,副教授、硕士生导师、工学博士.主要研究方向为智能空间技术、复杂系统建模与智能优化算法等.参与国家及省部级科研项目10余项,获省科技进步二等奖1项,发表学术论文40余篇.
更新日期/Last Update: 2010-03-31