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Conference item

Nonparametric Bayesian models for unsupervised activity recognition and tracking

Abstract:

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
Version:
Accepted manuscript

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Publisher copy:
10.1109/IROS.2016.7759595

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Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Kellogg College
Role:
Author
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Publication date:
2017-12-01
Acceptance date:
2016-07-01
DOI:
EISSN:
2153-0866
ISSN:
2153-0858
Pubs id:
pubs:668338
URN:
uri:04078ed8-830a-427e-b57a-f205f82edaf7
UUID:
uuid:04078ed8-830a-427e-b57a-f205f82edaf7
Local pid:
pubs:668338
ISBN:
9781509037629

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