Human action recognition based on chaotic invariants
A new human action recognition approach was presented based on chaotic invariants and relevance vector machines (RVM). The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action. The C-C method was used for e...
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| Published in | Journal of Central South University Vol. 20; no. 11; pp. 3171 - 3179 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2013
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| Subjects | |
| Online Access | Get full text |
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| Abstract | A new human action recognition approach was presented based on chaotic invariants and relevance vector machines (RVM). The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action. The C-C method was used for estimating delay time and embedding dimension of a phase space which was reconstructed by each trajectory. Then, some chaotic invariants representing action can be captured in the reconstructed phase space. Finally, RVM was used to recognize action. Experiments were performed on the KTH, Weizmann and Ballet human action datasets to test and evaluate the proposed method. The experiment results show that the average recognition accuracy is over 91.2%, which validates its effectiveness. |
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| AbstractList | A new human action recognition approach was presented based on chaotic invariants and relevance vector machines (RVM). The trajectories of reference joints estimated by skeleton graph matching were adopted for representing the nonlinear dynamical system of human action. The C-C method was used for estimating delay time and embedding dimension of a phase space which was reconstructed by each trajectory. Then, some chaotic invariants representing action can be captured in the reconstructed phase space. Finally, RVM was used to recognize action. Experiments were performed on the KTH, Weizmann and Ballet human action datasets to test and evaluate the proposed method. The experiment results show that the average recognition accuracy is over 91.2%, which validates its effectiveness. |
| Author | Tan, Lun-zheng Huang, Jin-xia Xia, Li-ming |
| Author_xml | – sequence: 1 givenname: Li-ming surname: Xia fullname: Xia, Li-ming email: xlm@csu.edu.cn organization: School of Information Science and Engineering, Central South University – sequence: 2 givenname: Jin-xia surname: Huang fullname: Huang, Jin-xia organization: School of Information Science and Engineering, Central South University – sequence: 3 givenname: Lun-zheng surname: Tan fullname: Tan, Lun-zheng organization: School of Information Science and Engineering, Central South University |
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| CitedBy_id | crossref_primary_10_1016_j_chaos_2019_109550 crossref_primary_10_1016_j_eswa_2015_04_039 crossref_primary_10_1109_TCDS_2016_2577044 crossref_primary_10_1016_j_aeue_2015_12_016 crossref_primary_10_2478_jee_2019_0077 crossref_primary_10_3233_JIFS_212088 crossref_primary_10_1007_s11771_015_2885_z crossref_primary_10_1016_j_cogsys_2019_12_004 crossref_primary_10_1155_2021_6634156 crossref_primary_10_1007_s11042_018_6923_3 crossref_primary_10_1007_s11771_015_2515_9 crossref_primary_10_1155_2014_495071 |
| Cites_doi | 10.1109/TPAMI.2007.70769 10.1016/j.imavis.2010.09.001 10.1016/S0167-2789(98)00240-1 10.1016/0167-2789(93)90009-P 10.1016/j.imavis.2009.11.014 10.1145/1922649.1922653 10.1109/TPAMI.2008.284 10.1007/s11771-012-1257-1 10.1109/TPAMI.2007.59 10.1109/CVPR.1997.609382 10.1016/j.patcog.2007.12.008 10.5565/rev/elcvia.163 |
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| Keywords | dynamic time wrapping (DTW) relevance vector machines (RVM) chaotic system action recognition chaotic invariants |
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