Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2015

Abstract

Tracking and modeling huge amount of users’ movement in a multi-floor building by using wireless devices is a challenging task, due to crowd movement complexity and signal sensing accuracy. In this paper, we use Layered Hidden Markov Model (LHMM) to fit the spatial-temporal trajectories (with large number of missing values). We decompose the problem into distinct layers that Hidden Markov Models (HMMs) are operated at different spatial granularities separately. Baum-Welch algorithm and Viterbi algorithm are used for finding the probable location sequences at each layer. By measuring the predicted result of trajectories, we compared the predicted results of both single standards HMM and multiple levels LHMM though 2D/3D path plotting, execution time and trajectory distance. The results indicate that LHMMs are better than HMMs for modeling and predicting the incomplete, long-distance temporal-spatial trajectories data.

Keywords

Layered Hidden Markov Model (LHMM), trajectory sensing, trajectory modelling, mobility recognition

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

2015 IEEE/WIC/ACM International Conference on Intelligent Agent Technology: Singapore, 6-9 December: Proceedings

First Page

344

Last Page

351

ISBN

9781467396189

Identifier

10.1109/WI-IAT.2015.239

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1109/WI-IAT.2015.239

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