BROWSE
Detail
Journal Article
Hierarchically Linked Infinite Hidden Markov Model based Trajectory Analysis and Semantic Region Retrieval in a Trajectory Dataset
Cited 12 time in 
Share 
Share
- Authors
- Yongjin Kwon, Kyuchang Kang, Junho Jin, Jinyoung Moon, Jongyoul Park
- Issue Date
- 2017-07
- Citation
- Expert Systems with Applications, v.78, pp.386-395
- ISSN
- 0957-4174
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- With an increasing attempt of finding latent semantics in a video dataset, trajectories have become key components since they intrinsically include concise characteristics of object movements. An approach to analyze a trajectory dataset has concentrated on semantic region retrieval, which extracts some regions in which have their own patterns of object movements. Semantic region retrieval has become an important topic since the semantic regions are useful for various applications, such as activity analysis. The previous literatures, however, have just revealed semantically relevant points, rather than actual regions, and have less consideration of temporal dependency of observations in a trajectory. In this paper, we propose a novel model for trajectory analysis and semantic region retrieval. We first extend the meaning of semantic regions that can cover actual regions. We build a model for the extended semantic regions based on a hierarchically linked infinite hidden Markov model, which can capture the temporal dependency between adjacent observations, and retrieve the semantic regions from a trajectory dataset. In addition, we propose a sticky extension to diminish redundant semantic regions that occur in a non-sticky model. The experimental results demonstrate that our models well extract semantic regions from a real trajectory dataset.
- KSP Keywords
- Hidden markov model(HMM), Key Components, Latent semantics, Novel model, Regions based, Semantic region, Temporal dependencies, Trajectory Analysis, Video dataset, activity analysis, model-based