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Fall Detection in RGB-D Videos for Elderly Care

Yixiao Yun (Institutionen för signaler och system, Signalbehandling) ; Christopher Innocenti (Institutionen för signaler och system) ; Gustav Nero (Institutionen för signaler och system) ; Henrik Lindén (Institutionen för signaler och system) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
17th IEEE Int'l conf. on E-Health, Networking, Application & Services (HealthCom'15), 2015 p. 6. (2015)
[Konferensbidrag, refereegranskat]

This paper addresses issues in fall detection from videos. Since it has been a broadly accepted intuition that a falling person usually undergoes large physical movement and displacement in a short time interval, the study is thus focused on measuring the intensity and temporal variation of pose change and body motion. The main novelties of this paper include: (a) characterizing pose/motion dynamics based on centroid velocity, head-to-centroid distance, histogram of oriented gradients and optical flow; (b) extracting compact features based on the mean and variance of pose/motion dynamics; (c) detecting human by combining depth information and background mixture models. Experiments have been conducted on an RGB-D video dataset for fall detection. Tests and evaluations show the effectiveness of the proposed method.

Nyckelord: Fall detection, Shape, appearance, optical flow, RGB-D video, elderly care, healthcare



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Denna post skapades 2015-10-30. Senast ändrad 2016-08-23.
CPL Pubid: 225097