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Article: Adaptive human motion analysis and prediction

TitleAdaptive human motion analysis and prediction
Authors
KeywordsAuto regressive models
Human motion analysis
Human motions
Long-term future
Long-term prediction
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr
Citation
Pattern Recognition, 2011, v. 44 n. 12, p. 2902-2914 How to Cite?
Abstract
Human motion analysis and prediction is an active research area where predicting human motion is often performed for a single time step based on historical motion. In recent years, longer term human motion prediction has been attempted over a number of future time steps. Most current methods learn motion patterns (MPs) from observed trajectories and then use them for prediction. However, these learned MPs may not be indicative due to inadequate observation, which naturally affects the reliability of motion prediction. In this paper, we present an adaptive human motion analysis and prediction method. It adaptively predicts motion based on the classified MPs in terms of their credibility, which refers to how indicative the learned MPs are for the specific environment. The main contributions of the proposed method are as follows: First, it provides a comprehensive description of MPs including not only the learned MPs but also their evaluated credibility. Second, it predicts long-term future motion with reasonable accuracy. A number of experiments have been conducted in simulated scenes and real-world scenes and the prediction results have been quantitatively evaluated. The results show that the proposed method is effective and superior in its performance when compared with a recursively applied Auto-Regressive (AR) model, which is called the Recursive Short-term Predictor (RSP) for long-term prediction. The proposed method has 17.73% of improvement over the RSP in prediction accuracy in the experiment with the best performance. On average, the proposed method has 5% improvement over the RSP in prediction accuracy over 10 experiments. © 2011 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/155622
ISSN
2013 Impact Factor: 2.584
2013 SCImago Journal Rankings: 1.653
ISI Accession Number ID
Funding AgencyGrant Number
Research Grant Council of the Hong Kong Special Administrative Region, ChinaHKU719406E
HKU719608E
University of Hong Kong
Funding Information:

The work on MP prediction was supported in part by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China, under Project HKU719406E and in part by the Postgraduate Studentship of The University of Hong Kong. The work on trajectory extraction was supported in part by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China, under Project HKU719608E and in part by the Postgraduate Studentship of the University of Hong Kong.

References

 

DC FieldValueLanguage
dc.contributor.authorChen, Zen_US
dc.contributor.authorWang, Len_US
dc.contributor.authorYung, NHCen_US
dc.date.accessioned2012-08-08T08:34:25Z-
dc.date.available2012-08-08T08:34:25Z-
dc.date.issued2011en_US
dc.identifier.citationPattern Recognition, 2011, v. 44 n. 12, p. 2902-2914en_US
dc.identifier.issn0031-3203en_US
dc.identifier.urihttp://hdl.handle.net/10722/155622-
dc.description.abstractHuman motion analysis and prediction is an active research area where predicting human motion is often performed for a single time step based on historical motion. In recent years, longer term human motion prediction has been attempted over a number of future time steps. Most current methods learn motion patterns (MPs) from observed trajectories and then use them for prediction. However, these learned MPs may not be indicative due to inadequate observation, which naturally affects the reliability of motion prediction. In this paper, we present an adaptive human motion analysis and prediction method. It adaptively predicts motion based on the classified MPs in terms of their credibility, which refers to how indicative the learned MPs are for the specific environment. The main contributions of the proposed method are as follows: First, it provides a comprehensive description of MPs including not only the learned MPs but also their evaluated credibility. Second, it predicts long-term future motion with reasonable accuracy. A number of experiments have been conducted in simulated scenes and real-world scenes and the prediction results have been quantitatively evaluated. The results show that the proposed method is effective and superior in its performance when compared with a recursively applied Auto-Regressive (AR) model, which is called the Recursive Short-term Predictor (RSP) for long-term prediction. The proposed method has 17.73% of improvement over the RSP in prediction accuracy in the experiment with the best performance. On average, the proposed method has 5% improvement over the RSP in prediction accuracy over 10 experiments. © 2011 Elsevier Ltd. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pren_US
dc.relation.ispartofPattern Recognitionen_US
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 2011, v. 44 n. 12, p. 2902-2914. DOI: 10.1016/j.patcog.2011.04.022-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAuto regressive modelsen_US
dc.subjectHuman motion analysisen_US
dc.subjectHuman motionsen_US
dc.subjectLong-term futureen_US
dc.subjectLong-term prediction-
dc.titleAdaptive human motion analysis and predictionen_US
dc.typeArticleen_US
dc.identifier.emailChen, Z: zchen@eee.hku.hken_US
dc.identifier.emailWang, L: wanglu@eee.hku.hk-
dc.identifier.emailYung, NHC: nyung@eee.hku.hk-
dc.identifier.authorityYung, NHC=rp00226en_US
dc.description.naturepostprinten_US
dc.identifier.doi10.1016/j.patcog.2011.04.022en_US
dc.identifier.scopuseid_2-s2.0-79959362533en_US
dc.identifier.hkuros201090-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79959362533&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume44en_US
dc.identifier.issue12en_US
dc.identifier.spage2902en_US
dc.identifier.epage2914en_US
dc.identifier.eissn1873-5142-
dc.identifier.isiWOS:000292947000008-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridYung, NHC=7003473369en_US
dc.identifier.scopusauthoridWang, L=35728037200en_US
dc.identifier.scopusauthoridChen, Z=35228484900en_US
dc.identifier.citeulike9280508-

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