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

• Published in: Sensors (Basel, Switzerland) Vol. 15; no. 9; pp. 23004 - 23019
• Main Authors: Yang, Lei, Ren, Yanyun, Hu, Huosheng, Tian, Bo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.09.2015; MDPI
• 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; United States > US; China; depth images; Single-Gauss-Model; Dense spatio-temporal-context; fall detection
• DOI: 10.3390/s150923004
• ISSN: 1424-8220

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.