Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model
Directly modelling the inherent hierarchy and shared structures of human behaviours, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society Press
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/15305 |
| _version_ | 1848748857346752512 |
|---|---|
| author | Nguyen, Nam Phung, Dinh Venkatesh, Svetha Bui, Hung H. |
| author2 | Schnid, C. |
| author_facet | Schnid, C. Nguyen, Nam Phung, Dinh Venkatesh, Svetha Bui, Hung H. |
| author_sort | Nguyen, Nam |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Directly modelling the inherent hierarchy and shared structures of human behaviours, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model?s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modelling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM. |
| first_indexed | 2025-11-14T07:11:42Z |
| format | Conference Paper |
| id | curtin-20.500.11937-15305 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:11:42Z |
| publishDate | 2005 |
| publisher | IEEE Computer Society Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-153052022-10-20T04:40:06Z Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model Nguyen, Nam Phung, Dinh Venkatesh, Svetha Bui, Hung H. Schnid, C. Soatto, S. Tomasi, C. Directly modelling the inherent hierarchy and shared structures of human behaviours, we present an application of the hierarchical hidden Markov model (HHMM) for the problem of activity recognition. We argue that to robustly model and recognize complex human activities, it is crucial to exploit both the natural hierarchical decomposition and shared semantics embedded in the movement trajectories. To this end, we propose the use of the HHMM, a rich stochastic model that has been recently extended to handle shared structures, for representing and recognizing a set of complex indoor activities. Furthermore, in the need of real-time recognition, we propose a Rao-Blackwellised particle filter (RBPF) that efficiently computes the filtering distribution at a constant time complexity for each new observation arrival. The main contributions of this paper lie in the application of the shared-structure HHMM, the estimation of the model?s parameters at all levels simultaneously, and a construction of an RBPF approximate inference scheme. The experimental results in a real-world environment have confirmed our belief that directly modelling shared structures not only reduces computational cost, but also improves recognition accuracy when compared with the tree HHMM and the flat HMM. 2005 Conference Paper http://hdl.handle.net/20.500.11937/15305 10.1109/CVPR.2005.203 IEEE Computer Society Press fulltext |
| spellingShingle | Nguyen, Nam Phung, Dinh Venkatesh, Svetha Bui, Hung H. Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model |
| title | Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model |
| title_full | Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model |
| title_fullStr | Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model |
| title_full_unstemmed | Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model |
| title_short | Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model |
| title_sort | learning and detecting activities from movement trajectories using the hierarchical hidden markov model |
| url | http://hdl.handle.net/20.500.11937/15305 |