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

Sensors

Huosheng Hu¹  Bo Tian²  Lei Yang³  Yanyun Ren³ 

[1] School of Computer Science and Electrical Engineering, University of Essex, Colchester CO4 3SQ,UK;School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China;School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China;

关键词: fall detection;    depth images;    Single-Gauss-Model;    Dense spatio-temporal-context;   

DOI  :  10.3390/s150923004

来源: DOAJ

【 摘 要 】

In order to deal with the problem of projection occurring in fall detection withtwo-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.

【 授权许可】

Unknown