Indoor Pedestrian Trajectory Detection with LSTM Network

doi:10.1109/CSE-EUC.2017.122

	Indoor Pedestrian Trajectory Detection with LSTM Network
	Li, Jin1 ; Li, Qiang 2; Chen, Nanxi 2; Wang, Yingguan2
	2017
会议录名称	2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1
卷号	1
页码	651-654
发表状态	已发表
DOI	10.1109/CSE-EUC.2017.122
摘要	This paper proposes a novel technique to detect the main moving trajectory of indoor pedestrians. Based on Long Short Term Memory(LSTM) Network, this deep learning network is capable of learning the trajectory of human beings using indoor Wi-Fi positioning data. The data is collected by Wi-Fi detectors densely installed in a public building in the urban area, which can ensure the detection of any portable devices as long as the Wi-Fi module is turned on. Then the model works in the form of sequence modeling to learn the trajectory of the main stream extracted from massive pedestrian positioning data In compare with methods like Recurrent Neural Network (RNN) and Gated Recurrent Unit(GRU), there is an obvious performance improvement of this method
关键词	Deep Learning Long Short Term Memory Wi-Fi Position Recurrent Nuearal Network
会议地点	Guangzhou, Guangdong, China
会议日期	21-24 July 2017
URL	查看原文
收录类别	CPCI ; EI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000451195900110
出版者	IEEE
EI入藏号	20174704447926
EI主题词	Brain ; Deep learning ; Recurrent neural networks ; Trajectories ; Ubiquitous computing ; Wi-Fi ; Wireless local area networks (WLAN)
EI分类号	Biomedical Engineering:461.1 ; Ergonomics and Human Factors Engineering:461.4 ; Computer Software, Data Handling and Applications:723 ; Computer Applications:723.5
原始文献类型	Proceedings Paper
来源库	IEEE
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/13249
专题	信息科学与技术学院信息科学与技术学院_特聘教授组_王营冠组信息科学与技术学院_硕士生
通讯作者	Li, Jin
作者单位	1.ShanghaiTech Univ, Univ Chinese Acad Sci, Shanghai, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai, Peoples R China
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Li, Jin,Li, Qiang,Chen, Nanxi,et al. Indoor Pedestrian Trajectory Detection with LSTM Network[C]:IEEE,2017:651-654.