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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Bibliographic Details
Published inJournal of Central South University Vol. 20; no. 11; pp. 3171 - 3179
Main Authors Xia, Li-ming, Huang, Jin-xia, Tan, Lun-zheng
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2013
Subjects
Engineering
Metallic Materials
dynamic time wrapping (DTW)
relevance vector machines (RVM)
chaotic system
action recognition
chaotic invariants
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Summary: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.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-013-1841-z