Fall detection in RGB-D videos by combining shape and motion features
Paper i proceeding, 2016

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.

shape feature

elderly care

Fall detection

RGB-D videos

contour descriptor


Durga Priya Kumar

Chalmers, Signaler och system

Yixiao Yun

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

978-1-4799-9988-0 (ISBN)







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