Unsupervised human activity analysis for intelligent mobile robots

Duckworth, P, Hogg, DC orcid.org/0000-0002-6125-9564 and Cohn, AG orcid.org/0000-0002-7652-8907 (2019) Unsupervised human activity analysis for intelligent mobile robots. Artificial Intelligence, 270. pp. 67-92. ISSN 0004-3702



Copyright, Publisher and Additional Information: © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: Human activity analysis; Mobile robotics; Qualitative spatio-temporal representation; Low-rank approximations; Probabilistic machine learning; Latent Dirichlet allocation
  • Accepted: 9 December 2018
  • Published (online): 7 January 2019
  • Published: May 2019
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Funding Information:
FunderGrant number
EU - European UnionFP7-ICT-600623
Depositing User: Symplectic Publications
Date Deposited: 04 Jan 2019 11:15
Last Modified: 15 Feb 2019 16:11
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.artint.2018.12.005
Related URLs:

Share / Export