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Sensors 2015, 15(9), 23004-23019; doi:10.3390/s150923004

New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

1
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China
2
School of Computer Science and Electrical Engineering, University of Essex, Colchester CO4 3SQ, UK
3
School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
*
Author to whom correspondence should be addressed.
Academic Editor: Panicos Kyriacou
Received: 29 June 2015 / Revised: 31 July 2015 / Accepted: 2 September 2015 / Published: 11 September 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [964 KB, uploaded 18 September 2015]   |  

Abstract

In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity. View Full-Text
Keywords: fall detection; depth images; Single-Gauss-Model; Dense spatio-temporal-context fall detection; depth images; Single-Gauss-Model; Dense spatio-temporal-context
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yang, L.; Ren, Y.; Hu, H.; Tian, B. New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images. Sensors 2015, 15, 23004-23019.

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