基于时空模式的复杂行为识别方法研究

项目名称： 基于时空模式的复杂行为识别方法研究

项目编号： No.61771387

项目类型： 面上项目

立项/批准年度： 2018

项目学科： 无线电电子学、电信技术

项目作者： 李军怀

作者单位： 西安理工大学

项目金额： 16万元

中文摘要： 感知和识别复杂环境中人的行为是普适计算研究的热点和难点课题之一。本项目针对个体复杂行为描述、建模和识别等关键问题，运用模式识别、机器学习和数据挖掘等理论和方法，研究基于多传感器感知的复杂行为识别方法体系。首先，基于人的行为的差异性和多模性特点，研究多模式行为的自适应检测与数据划分方法；并通过分析行为模式、关键特征子集与分类算法三者之间的关联关系及特征之间的交互性，研究基于特征子集区分度的行为识别特征优选方法，进而研究基于时空特性和加权特征融合的原子动作识别方法。其次，通过分析人的行为特性和规律，研究原子动作间的时空相关性，构建基于时空模式的个体复杂行为描述与识别分层模型。在此基础上，通过挖掘构成复杂行为的原子动作间的频繁时空模式作为中间层特征，研究顺序、并发和交叉等类型复杂行为的识别方法，建立基于时空模式的复杂行为识别方法体系，为基于传感器的行为识别方法研究和应用提供新的思路和理论依据。

中文关键词： 传感器信息融合；传感器信息处理；时空模式；行为识别

英文摘要： Sensing and recognizing human behavior under complex environment has been heatedly discussed and widely researched in the area of pervasive computing. Focusing on key issues such as representation, modeling and recognizing complex individual behavior, this project studies the techniques to layering, representation and recognizing complex behavior from the aspect of behavior perception and recognition. It also researches on a multi-sensor perception based complex individual behavior recognition system, utilizing theories and methods from disciplines of pattern recognition, machine learning, data mining and human-machine interaction. It first studies self-adaptive window partition, feature extraction and optimization, as well weighted feature fusion-based atomic motion recognition method, through raw data acquisition of mobile objects in real-world by deploying multi-typed sensors. Based on the analysis of behavioral characteristics and patterns of mobile objects, the project then studies the spatio-temporal correlation among atomic activities, and constructs a hierarchal model to describe and recognize individual complex behavior under spatio-temporal environment. Finally, through mining frequent spatio-temporal patterns among complex atomic activities, the project researches on various types of complex behavior recognition methods such as sequential, concurrent and intersecting method. It also establishes a complex behavior recognition system under spatio-temporal environment, which provides a new perspective for future research on sensor-based activity recognition.

英文关键词： Sensor Information Fusion;Senor Information Processing;Spation-temporal Pattern;Activity Recognition