Article: Adaptive human motion analysis and prediction
| Title | Adaptive human motion analysis and prediction | ||||||
|---|---|---|---|---|---|---|---|
| Authors | Chen, Z1 Wang, L1 Yung, NHC1 | ||||||
| Keywords | Auto regressive models Human motion analysis Human motions Long-term future Long-term prediction | ||||||
| Issue Date | 2011 | ||||||
| Publisher | Elsevier 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?] DOI: http://dx.doi.org/10.1016/j.patcog.2011.04.022 | ||||||
| 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. | ||||||
| ISSN | 0031-3203 2011 Impact Factor: 2.292 2011 SCImago Journal Rankings: 0.119 | ||||||
| DOI | http://dx.doi.org/10.1016/j.patcog.2011.04.022 | ||||||
| ISI Accession Number ID | WOS:000292947000008
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 | References in Scopus |
| dc.contributor.author | Chen, Z | ||||||
|---|---|---|---|---|---|---|---|
| dc.contributor.author | Wang, L | ||||||
| dc.contributor.author | Yung, NHC | ||||||
| dc.date.accessioned | 2012-08-08T08:34:25Z | ||||||
| dc.date.available | 2012-08-08T08:34:25Z | ||||||
| dc.date.issued | 2011 | ||||||
| dc.description.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. | ||||||
| dc.description.nature | postprint | ||||||
| dc.identifier.citation | Pattern Recognition, 2011, v. 44 n. 12, p. 2902-2914 [How to Cite?] DOI: http://dx.doi.org/10.1016/j.patcog.2011.04.022 | ||||||
| dc.identifier.citeulike | 9280508 | ||||||
| dc.identifier.doi | http://dx.doi.org/10.1016/j.patcog.2011.04.022 | ||||||
| dc.identifier.epage | 2914 | ||||||
| dc.identifier.hkuros | 201090 | ||||||
| dc.identifier.isi | WOS:000292947000008
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. | ||||||
| dc.identifier.issn | 0031-3203 2011 Impact Factor: 2.292 2011 SCImago Journal Rankings: 0.119 | ||||||
| dc.identifier.issue | 12 | ||||||
| dc.identifier.scopus | eid_2-s2.0-79959362533 | ||||||
| dc.identifier.spage | 2902 | ||||||
| dc.identifier.uri | http://hdl.handle.net/10722/155622 | ||||||
| dc.identifier.volume | 44 | ||||||
| dc.language | eng | ||||||
| dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/pr | ||||||
| dc.publisher.place | Netherlands | ||||||
| dc.relation.ispartof | Pattern Recognition | ||||||
| dc.relation.references | References in Scopus | ||||||
| dc.rights | NOTICE: 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.rights | Creative Commons: Attribution 3.0 Hong Kong License | ||||||
| dc.subject | Auto regressive models | ||||||
| dc.subject | Human motion analysis | ||||||
| dc.subject | Human motions | ||||||
| dc.subject | Long-term future | ||||||
| dc.subject | Long-term prediction | ||||||
| dc.title | Adaptive human motion analysis and prediction | ||||||
| dc.type | Article |
Author Affiliations
- The University of Hong Kong