Conference item
Nonparametric Bayesian models for unsupervised activity recognition and tracking
- Abstract:
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Human locomotion and activity recognition systems form a critical part in a robot's ability to safely and effectively operate in a environment populated with human end users. Previous work in this area relies upon strong assumptions about the labels in the training data; e.g. that are noise-free and that they exist at all. Our approach does not predefine the relevant behaviours or their number, as both are learned directly from observations, similar to real-world human-robot interactions, whe...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Accepted manuscript, pdf, 638.4KB)
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- Publisher copy:
- 10.1109/IROS.2016.7759595
Authors
Bibliographic Details
- Publisher:
- Institute of Electrical and Electronics Engineers Publisher's website
- Host title:
- IROS 2016: IEEE International Conference on Intelligent Robots and Systems
- Journal:
- IROS 2016 Journal website
- Publication date:
- 2017-12-01
- Acceptance date:
- 2016-07-01
- DOI:
- EISSN:
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2153-0866
- ISSN:
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2153-0858
- ISBN:
- 9781509037629
Item Description
- Pubs id:
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pubs:668338
- UUID:
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uuid:04078ed8-830a-427e-b57a-f205f82edaf7
- Local pid:
- pubs:668338
- Source identifiers:
-
668338
- Deposit date:
- 2017-03-23
Terms of use
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2017
- Notes:
- © 2016 IEEE. This is the accepted manuscript version of the article. The final version is available online from IEEE at: [10.1109/IROS.2016.7759595]
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