有限状态自动机辅助的行人导航状态匹配算法

方志祥; 罗浩; 李灵

doi:10.11947/j.AGCS.2017.20160530

测绘学报 >

2017 , Vol. 46 >Issue 3: 371 - 380

DOI: https://doi.org/10.11947/j.AGCS.2017.20160530

地图学与地理信息

有限状态自动机辅助的行人导航状态匹配算法

方志祥 ,
罗浩 ,
李灵

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1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 四川省第二测绘地理信息工程院, 四川成都 610100

方志祥(1977-),男,教授,博士生导师,研究方向为行人导航、时空行为建模与应用。E-mail:zxfang@whu.edu.cn

收稿日期: 2016-10-24

修回日期: 2017-03-09

网络出版日期: 2017-04-11

基金资助

国家自然科学基金面上项目（413714420）

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A Finite State Machine Aided Pedestrian Navigation State Matching Algorithm

FANG Zhixiang ,
LUO Hao ,
LI Ling

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1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Second School in Surveying, Mapping and Geographic Information, Sichuan Province, Chengdu 610100, China

Received date: 2016-10-24

Revised date: 2017-03-09

Online published: 2017-04-11

Supported by

The National Natural Science Foundation of China (No. 413714420)

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摘要

行人导航状态的自动识别是行人导航研究的一个难点问题，对提升行人导航软件服务的精准反馈与改善导航性能至关重要，此方面已有的研究工作很少。本文提出了一种基于有限状态自动机的行人导航状态匹配算法，其核心思想是在识别行人动作基础上匹配行人当前导航状态。利用谷歌眼镜及智能手机采集的多种传感器数据对行人动作进行识别，得到其动作特征参数；然后将行人导航状态分为熟悉、陌生及迷路3类，根据有限状态自动机理论建立状态转移模型，设计基于该模型的行人导航状态匹配算法；最后，实现状态匹配算法，通过试验对该算法的有效性进行验证。试验结果表明，该算法能够较好地识别行人导航过程中的状态转移，其中对熟悉向陌生状态转移识别准确度较高，对迷路状态识别准确度达到90%。

关键词： 行人导航; 有限状态自动机; 动作识别; 状态匹配

本文引用格式

方志祥 , 罗浩 , 李灵 . 有限状态自动机辅助的行人导航状态匹配算法[J]. 测绘学报, 2017 , 46(3) : 371 -380 . DOI: 10.11947/j.AGCS.2017.20160530

Abstract

The automatic identification of pedestrian's navigation state is a difficult problem in pedestrian navigation research. It is important to improve the precision feedback and navigation performance of pedestrian navigation services, and few researches have been done in this field. This paper proposes a pedestrian navigation state matching algorithm based on finite state machine (FSM). The main idea of this method is to identify the pedestrian navigation state on the basis of recognizing pedestrian's actions. The pedestrian's action characteristics are recognized by using multiple sensor data collected by Google glass and mobile phone. Then, the pedestrian navigation states are divided into familiar, unfamiliar and lost state. The state transition model is established according to the FSM theory, and the pedestrian navigation state matching algorithm based on the model is designed. Finally, this algorithm is implemented, and experiments are conducted to validate its effectiveness. Experimental results show that the proposed algorithm can reach a good precision of recognizing the state transitions during pedestrian navigation process, and especially the accuracy of recognizing lost state achieves 90%.

Key words： pedestrian navigation; finite state machine; action recognition; navigation state matching

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