A Layered Hidden Markov Model for predicting human trajectories in a multi-floor building

• Main Authors: LI, Qian, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Layered Hidden Markov Model (LHMM); trajectory sensing; trajectory modelling; mobility recognition; Artificial Intelligence and Robotics; Computer Sciences; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2911; https://ink.library.smu.edu.sg/context/sis_research/article/3911/viewcontent/Layer_Hidden_Markov_2015_pv.pdf
• Institution: Singapore Management University

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.