An Eigenspace-Based Approach for Human Fall Detection using Integrated Time Motion Image and Neural Network

摘要：

Falls are a major health hazard for the elderly and a serious obstacle for independent living.Since falling causes dramatic physical-psychological consequences,development of intelligent video surveillance systems is so important due to providing safe environments.To this end,this paper proposes a novel approach for human fall detection based on combination of integrated time motion images and eigenspace technique.Integrated Time Motion Image (ITMI) is a type of spatio-temporal database that includes motion and time of motion occurrence.Applying eigenspace technique to ITMIs leads in extracting eigen-motion and finally MLP Neural Network is used for precise classification of motions and determination of a fall event.Unlike existent fall detection systems only deal with limited movement patterns,we considered wide range of motions consisting normal daily life activities,abnormal behaviors and also unusual events.Reliable recognition rate of experimental results underlines satisfactory performance of our system.

作者: Homa Foroughi Aabed Naseri Alireza Saberi Hadi Sadoghi Yazdi

作者单位: Department of Computer Engineering,Ferdowsi University of Mashhad,Iran Department of Electrical and Computer Engineering,Concordia University,Canada

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）