Human action recognition based on chaotic invariants
来源期刊：中南大学学报(英文版)2013年第11期
论文作者：XIA Li-ming(夏利民) HUANG Jin-xia(黄金霞) TAN Lun-zheng(谭论正)
文章页码：3171 - 3179
Key words：chaotic system; action recognition; chaotic invariants; dynamic time wrapping (DTW); relevance vector machines (RVM)
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.
XIA Li-ming(夏利民), HUANG Jin-xia(黄金霞), TAN Lun-zheng(谭论正)
(School of Information Science and Engineering, Central South University, Changsha 410083, China)
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.
Key words:chaotic system; action recognition; chaotic invariants; dynamic time wrapping (DTW); relevance vector machines (RVM)
