Hierarchically linked infinite hidden Markov model based trajectory analysis and semantic region retrieval in a trajectory dataset
作者：
Highlights：
• A novel model for trajectories and semantic regions (sest-hiHMM) is proposed.
• A sticky version of sest-hiHMMs is proposed for reducing redundant semantic regions.
• An extended definition of semantic regions covers actual regions, not sets of points.
• Our models concern the temporal dependency of observations in a trajectory.
• Our models retrieve reasonable semantic regions from a real trajectory dataset.
摘要
•A novel model for trajectories and semantic regions (sest-hiHMM) is proposed.•A sticky version of sest-hiHMMs is proposed for reducing redundant semantic regions.•An extended definition of semantic regions covers actual regions, not sets of points.•Our models concern the temporal dependency of observations in a trajectory.•Our models retrieve reasonable semantic regions from a real trajectory dataset.
论文关键词：Trajectory analysis,Semantic regions,Nonparametric Bayesian models,Infinite hidden Markov models,Sticky extensions
论文评审过程：Received 9 November 2016, Revised 14 February 2017, Accepted 15 February 2017, Available online 16 February 2017, Version of Record 25 February 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.02.026
