Semantic trajectory extraction framework for indoor space

LUO Xinyuan; CHEN Xin; SHOU Lidan; CHEN Ke; WU Yanjing

doi:10.16511/j.cnki.qhdxxb.2018.26.047

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Journal of Tsinghua University(Science and Technology) ›› 2019, Vol. 59 ›› Issue (3) : 186-193. DOI: 10.16511/j.cnki.qhdxxb.2018.26.047

COMPUTER SCIENCE AND TECHNOLOGY

Semantic trajectory extraction framework for indoor space

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Abstract

Using massive location data to analyze user behavior and tap the potential value of customers is getting much attention. Mature solutions have been studied for outdoor space, while the accuracy and robustness of WiFi positioning data are main challenges for indoor space. The method of extracting the semantic trajectory for indoor space is studied. It can reduce error, compress original position data and enhance the expression capability of trajectory for indoor spatio-temporal data mining. Based on four modules including indoor space modeling, data cleansing, event extraction and semantic enhancement, a framework for calculating indoor semantic trajectory is proposed. Experiments are conducted on real and simulated datasets, and the results show that this method can accurately and effectively extract trajectory data containing semantic information from indoor positioning data with incompleteness and errors, which can be used for high-level analytical applications.

Key words

indoor positioning / indoor space model / semantic trajectory / density clustering

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LUO Xinyuan, CHEN Xin, SHOU Lidan, CHEN Ke, WU Yanjing. Semantic trajectory extraction framework for indoor space[J]. Journal of Tsinghua University(Science and Technology). 2019, 59(3): 186-193 https://doi.org/10.16511/j.cnki.qhdxxb.2018.26.047

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