Indoor Mobility Semantics Annotation Using Coupled Conditional Markov Networks

Huan Li, Hua Lu, Muhammad Aamir Cheema, Lidan Shou, Gang Chen

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Abstrakt

Indoor mobility semantics analytics can greatly benefit many pertinent applications. Existing semantic annotation methods mainly focus on outdoor space and require extra knowledge such as POI category or human activity regularity. However, these conditions are difficult to meet in indoor venues with relatively small extents but complex topology. This work studies the annotation of indoor mobility semantics that describe an object’s mobility event (what) at a semantic indoor region (where) during a time period (when). A coupled conditional Markov network (C2MN) is proposed with a set of feature functions carefully designed by incorporating indoor topology and mobility behaviors. C2MN is able to capture probabilistic dependencies among positioning records, semantic regions, and mobility events jointly. Nevertheless, the correlation of regions and events hinders the parameters learning. Therefore, we devise an alternate learning algorithm to enable the parameter learning over correlated variables. The extensive experiments demonstrate that our C2MN-based semantic annotation is efficient and effective on both real and synthetic indoor mobility data.
OriginalsprogEngelsk
TitelThe 36th IEEE International Conference on Data Engineering (ICDE 2020)
Antal sider12
ForlagIEEE
Publikationsdatoapr. 2020
Sider1441-1452
Artikelnummer9101369
ISBN (Trykt)978-1-7281-2904-4
ISBN (Elektronisk)9781728129037
DOI
StatusUdgivet - apr. 2020
Begivenhed36th IEEE International Conference on Data Engineering - Dallas, USA
Varighed: 20 apr. 202024 apr. 2020
https://www.utdallas.edu/icde/

Konference

Konference36th IEEE International Conference on Data Engineering
LandUSA
ByDallas
Periode20/04/202024/04/2020
Internetadresse

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