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An indoor semantic trajectory labeling and completion method in a low sampling location environment

A low-sampling, semantic technology, applied in the field of indoor semantic trajectory annotation and completion, which can solve the problems of difficult and unobserved position inference, and the accuracy of labeling entities cannot reach analytical applications.

Active Publication Date: 2019-01-11
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

The traditional labeling methods for geospatial events and geographical location names are difficult to infer the unobserved location of objects when faced with indoor low-sampled location data, and the accuracy of labeled entities cannot meet the general requirements of analysis applications

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  • An indoor semantic trajectory labeling and completion method in a low sampling location environment
  • An indoor semantic trajectory labeling and completion method in a low sampling location environment
  • An indoor semantic trajectory labeling and completion method in a low sampling location environment

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Embodiment Construction

[0057] The technical solution of the present invention will be further described in conjunction with specific implementation and examples.

[0058] Such as figure 1 , specific embodiments of the present invention and its implementation process are as follows:

[0059] Step 1: First, enter the user-defined semantic entities and indoor spatial structure information, and use the semantic entities combined with the indoor spatial structure information to construct a mobile transition graph with mobile transition probability values.

[0060] Semantic entities in this example include semantic regions and event patterns.

[0061] Semantic regions can be defined with reference to indoor spatial structure information. For example, in a shopping mall, all the resident stores can be defined as semantic regions. The key features of a semantic region include the geometric range, name, label, and description information of the semantic region, as well as the topological connectivity and ...

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Abstract

The invention discloses an indoor semantic trajectory labeling and completion method in a low sampling and positioning environment. The method includes the following steps: inputting semantic entity and indoor spatial structure information to construct the mobile transfer map; collecting the original position trajectory and dividing each original position trajectory into a plurality of segmented segments; for the event mode, the user selecting the bound position trajectory segment; comparing the similarities of the key features of the segmented fragments and the location trajectory fragments,and linking the most similar fragments to form a group of independent semantic tuples; assembling the semantic tuples, and generating the missing semantic tuples by probabilistic inference; exportingthe complete semantic trajectory after insertion and completion. The method of the invention can derive accurate and available complete semantic trajectory data when facing the indoor positioning scene with complex movement constraint and low sampling frequency of position data.

Description

technical field [0001] The invention relates to time series data segmentation technology, probability inference technology and semantic extraction technology in the field of spatio-temporal data mining, in particular to an indoor semantic trajectory labeling and completion method in a low-sampling positioning environment. Background technique [0002] In recent years, with the popularization of smart terminals and the rapid development of indoor positioning infrastructure, indoor location data is growing at an unprecedented rate. Rich indoor location data records the movement characteristics of a large number of pedestrians and devices in indoor environments. Through the effective analysis of these mobile characteristics, important mobile knowledge can be obtained to help data analysts gain insight into the behavior patterns of target groups in indoor environments. However, indoor location data only records the spatial coordinates of the target object, and lacks semantic in...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06F16/909
CPCG06F18/23G06F18/214
Inventor 寿黎但李环陈珂骆歆远陈刚江大伟胡天磊
Owner ZHEJIANG UNIV
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