Human action recognition based on chaotic invariants

被引：0
作者：
Li-ming Xia
Jin-xia Huang
Lun-zheng Tan
机构：
[1] Central South University,School of Information Science and Engineering
来源：
Journal of Central South University | 2013年 / 20卷
关键词：
chaotic system; action recognition; chaotic invariants; dynamic time wrapping (DTW); relevance vector machines (RVM);
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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收藏
页码：3171 / 3179
页数：8
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