面向家庭智能空间主动服务的目标行为分析与识别
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摘要
随着家庭智能空间技术、服务机器人技术的发展，针对家庭环境感知及主动智能服务问题，越来越多的研究开始关注于服务对象行为分析和理解等方向。其重点是分析服务对象日常行为规律，判断所处行为状态和需要，进而为其提供主动服务。
不同于固定场所的特定行为监控，人们日常生活的家庭环境存在多样性、异质性和动态性等特点，利用现有的行为模式研究成果难以解决家庭环境下服务对象行为识别问题。本文针对家庭环境中具有正常行为能力且生活有规律的服务对象，就全局位置行为模型建立、动作行为特征提取、多视角动作识别及自然行为理解等多个具体问题展开研究。
首先，分析了服务对象的日常活动规律，基于环境标志点拟合位置移动轨迹，建立服务对象全局位置行为习惯模型。同时，进一步引入状态驻留时间参数优化模型结构，进而验证服务对象在家庭环境中的位置行为规律，并提供主动位置状态预测及异常判断服务。
其次，基于位置和动作特征，分析动作行为在不同位置的分布规律。提出一种动作差分能量图的姿态图像序列表示方法，引入局部相对二阶矩量化动作特征，并通过基于免疫机制的模糊均值聚类算法对位置、动作联合特征向量进行聚类，获取目标在不同位置的动作行为分布。
再次，针对动作行为识别问题，设计一种用基于关键姿态的关节点标定和特征表示方法。通过圆形矩描述子对动作行为姿态图像序列筛选得到关键姿态集合，并对其进行关节点标定，将加权关节点相对坐标作为姿态特征，识别理想视角下的动作行为。
然后，考虑目标动作行为往往具有时间延续性和视角空间延续性，选用时空概率图作为模型工具，用多视角分布空间依赖和相邻动作的时间延续解决多视角下连续动作识别问题，建立基于关键姿态关节点特征的多视角动作行为模型，给出模型的训练和推断方法。
最后，结合前面提出的全局位置行为表示和多视角局部动作行为识别的研究，探讨家庭环境下高层行为推理机制。融合时间、位置、动作和生理参数，提出一种动态权值信度合成证据理论的自然行为识别方法，同时进一步深化主动行为预测服务和主动异常行为判断机制。
With the development of intelligent home space and service robotics, more and moreresearch focuses on the analysis and understanding of service object behaviour accordingto home environment perception and active intelligence service. The key is the analysis ofdaily behavior rules and the judgment of behavior status and needs for service object, thenprovide active service.
In different from specific behavior monitoring of fixed spaces, home environment ofdaily life has the characteristics of diversity, heterogeneity and dynamic. The researchresults existed for human behavior mode is difficult to solve the problem of service objectbehavior recognition problem in family environment. In this thesis, according to theservice object in family with normal capacity and living habits, much research work hasbeen done on the behavioral model of global location, feature extraction of action,multi-view action recognition and understanding of the natural behavior.
Firstly, the regular pattern of the daily activies of the service object are analyzed, andthen environmental mark points are used in fitting position trajectory in order to establishbehavior model based on global position. At the same time, time stay parametes is addedto optimize the model and verify location behavior of service objects in the homeenvironment, then provides the prediction of active location mode and abnormal judgmentservice.
Secondly, based on the characteristics of position and action, the regularity of actiondistribution in different position is discussed. A method of action presention based ondifferent energy image in human pose sequence is given and the local relative twomoments is used to present the characteristics of action, then the combined characteristicsof location and action are clustered through the FCM algorithm based on immunemechanism to obtain the distribution of the object action in different location.
Once again, according to the problems of action behavior recognition, arepresentation method for joint calibration and feature extration based on key posture isimplemented. Through circular moment descriptors, the key postures are got from theimage sequence of action behavior and finished point calibration, then the relative weighted joint point of the gesture characteristic is used in action behavior recognitionwith ideal perspective.
Then, considering the target actions often have time continuity and the perspective ofspatial continuity, then the space-time probability map can be used as a modeling tool, thespatial distribution of dependence and the adjacent motion with multi-angle are extendedto solve continuous action recognition problem under different perspectives, and themulti-view behavior model of key posture joint based on feature point is the established.
Finally, combined with the global position behavior represention and the recognitionof multi-view behavior previously proposed, the high-level behavior in the homeenvironment is analyzed. Fusing time, location, action and physiological parameters, theidentification methods are proposed based on dynamic weights reliability of D-S theory tofurther deepen the service of proactive behavior forecasting and the mechanism ofabnormal behavior judgment.
不同于固定场所的特定行为监控，人们日常生活的家庭环境存在多样性、异质性和动态性等特点，利用现有的行为模式研究成果难以解决家庭环境下服务对象行为识别问题。本文针对家庭环境中具有正常行为能力且生活有规律的服务对象，就全局位置行为模型建立、动作行为特征提取、多视角动作识别及自然行为理解等多个具体问题展开研究。
首先，分析了服务对象的日常活动规律，基于环境标志点拟合位置移动轨迹，建立服务对象全局位置行为习惯模型。同时，进一步引入状态驻留时间参数优化模型结构，进而验证服务对象在家庭环境中的位置行为规律，并提供主动位置状态预测及异常判断服务。
其次，基于位置和动作特征，分析动作行为在不同位置的分布规律。提出一种动作差分能量图的姿态图像序列表示方法，引入局部相对二阶矩量化动作特征，并通过基于免疫机制的模糊均值聚类算法对位置、动作联合特征向量进行聚类，获取目标在不同位置的动作行为分布。
再次，针对动作行为识别问题，设计一种用基于关键姿态的关节点标定和特征表示方法。通过圆形矩描述子对动作行为姿态图像序列筛选得到关键姿态集合，并对其进行关节点标定，将加权关节点相对坐标作为姿态特征，识别理想视角下的动作行为。
然后，考虑目标动作行为往往具有时间延续性和视角空间延续性，选用时空概率图作为模型工具，用多视角分布空间依赖和相邻动作的时间延续解决多视角下连续动作识别问题，建立基于关键姿态关节点特征的多视角动作行为模型，给出模型的训练和推断方法。
最后，结合前面提出的全局位置行为表示和多视角局部动作行为识别的研究，探讨家庭环境下高层行为推理机制。融合时间、位置、动作和生理参数，提出一种动态权值信度合成证据理论的自然行为识别方法，同时进一步深化主动行为预测服务和主动异常行为判断机制。
With the development of intelligent home space and service robotics, more and moreresearch focuses on the analysis and understanding of service object behaviour accordingto home environment perception and active intelligence service. The key is the analysis ofdaily behavior rules and the judgment of behavior status and needs for service object, thenprovide active service.
In different from specific behavior monitoring of fixed spaces, home environment ofdaily life has the characteristics of diversity, heterogeneity and dynamic. The researchresults existed for human behavior mode is difficult to solve the problem of service objectbehavior recognition problem in family environment. In this thesis, according to theservice object in family with normal capacity and living habits, much research work hasbeen done on the behavioral model of global location, feature extraction of action,multi-view action recognition and understanding of the natural behavior.
Firstly, the regular pattern of the daily activies of the service object are analyzed, andthen environmental mark points are used in fitting position trajectory in order to establishbehavior model based on global position. At the same time, time stay parametes is addedto optimize the model and verify location behavior of service objects in the homeenvironment, then provides the prediction of active location mode and abnormal judgmentservice.
Secondly, based on the characteristics of position and action, the regularity of actiondistribution in different position is discussed. A method of action presention based ondifferent energy image in human pose sequence is given and the local relative twomoments is used to present the characteristics of action, then the combined characteristicsof location and action are clustered through the FCM algorithm based on immunemechanism to obtain the distribution of the object action in different location.
Once again, according to the problems of action behavior recognition, arepresentation method for joint calibration and feature extration based on key posture isimplemented. Through circular moment descriptors, the key postures are got from theimage sequence of action behavior and finished point calibration, then the relative weighted joint point of the gesture characteristic is used in action behavior recognitionwith ideal perspective.
Then, considering the target actions often have time continuity and the perspective ofspatial continuity, then the space-time probability map can be used as a modeling tool, thespatial distribution of dependence and the adjacent motion with multi-angle are extendedto solve continuous action recognition problem under different perspectives, and themulti-view behavior model of key posture joint based on feature point is the established.
Finally, combined with the global position behavior represention and the recognitionof multi-view behavior previously proposed, the high-level behavior in the homeenvironment is analyzed. Fusing time, location, action and physiological parameters, theidentification methods are proposed based on dynamic weights reliability of D-S theory tofurther deepen the service of proactive behavior forecasting and the mechanism ofabnormal behavior judgment.
引文
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