3D Human Action Recognition and Style Transformation Using Resilient Backpropagation Neural Networks

摘要：

This paper addresses the problem of 3D human action class and style class recognition as well as style transformations using Artificial Neural Networks. The training process is selected uniquely to suit the problem and a quantitative evaluation method is proposed for the results. Few other intelligent methods have also been applied for recognition and compared to our original approach. The results demonstrate the high classification and transformation precision of our method, while both tasks are performed using the same system.

关键词： Human action Neural networks Recognition Resilient backpropagation Re-synthesis

作者: Seyed Ali Etemad Ali Arya

作者单位: Department of Systems and Computer Engineering Carleton University Ottawa,Canada School of Information Technology Carleton University Ottawa,Canada

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 2825-2830

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）