CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Fall detection in RGB-D videos by combining shape and motion features

Durga Priya Kumar (Institutionen för signaler och system) ; Yixiao Yun (Institutionen för signaler och system, Signalbehandling) ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016; Shanghai International Convention CenterShanghai; China; 20 March 2016 through 25 March 2016 (1520-6149). p. 1337-1341. (2016)
[Konferensbidrag, refereegranskat]

This paper addresses issues in fall detection from RGB-D videos. The study focuses on measuring the dynamics of shape and motion of the target person, based on the observation that a fall usually causes drastic large shape deformation and physical movement. The main novelties include: (a) forming contours of target persons in depth images based on morphological skeleton; (b) extracting local dynamic shape and motion features from target contours; (c) encoding global shape and motion in HOG and HOGOF features from RGB images; (d) combining various shape and motion features for enhanced fall detection. Experiments have been conducted on an RGB-D video dataset for fall detection. Results show the effectiveness of the proposed method.

Nyckelord: contour descriptor; elderly care; Fall detection; RGB-D videos; shape feature



Denna post skapades 2016-07-12.
CPL Pubid: 239282

 

Läs direkt!


Länk till annan sajt (kan kräva inloggning)