Indoor Mobility Semantics Annotation Using Coupled Conditional Markov Networks

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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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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.
Original languageEnglish
Title of host publicationThe 36th IEEE International Conference on Data Engineering (ICDE 2020)
Number of pages12
Publication dateApr 2020
Article number9101369
ISBN (Print)978-1-7281-2904-4
ISBN (Electronic)9781728129037
Publication statusPublished - Apr 2020
Event36th IEEE International Conference on Data Engineering - Dallas, United States
Duration: 20 Apr 202024 Apr 2020


Conference36th IEEE International Conference on Data Engineering
CountryUnited States
Internet address
SeriesProceedings of the International Conference on Data Engineering


  • Semantic annotation
  • Indoor mobility data
  • Markov random fields

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