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

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2015-09, Vol.15 (9), p.23004-23019
Main Authors: Yang, Lei, Ren, Yanyun, Hu, Huosheng, Tian, Bo
Format: Article
Language:English
Subjects:
Accidental Falls
Adult
Algorithms
Computer engineering
Dense spatio-temporal-context
depth images
fall detection
Falls
Head - physiology
Home environment
Human subjects
Humans
Image detection
Imaging, Three-Dimensional - methods
Mathematical analysis
Methods
Older people
Planes
Sensors
Single-Gauss-Model
Three dimensional
Tracking
Two dimensional
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s150923004