Three-States-Transition Method for Fall Detection Algorithm Using Depth Image

Abstract

Currently, the proportion of elderly persons is increasing all over the world, and accidents involving falls have become a serious problem especially for those who live alone. In this paper, an enhancement to our algorithm to detect such falls in an elderly person’s living room is proposed. Our previous algorithm obtains a binary image by using a depth camera and obtains an outline of the binary image by Canny edge detection. This algorithm then calculates the tangent vector angles of each outline pixels and divide them into 15° range groups. If most of the tangent angles are below 45°, a fall is detected. Traditional fall detection systems cannot detect falls towards the camera so at least two cameras are necessary in related works. To detect falls towards the camera, this study proposes the addition of a three-states-transition method to distinguish a fall state from a sitting-down one. The proposed algorithm computes the different position states and divides these states into three groups to detect the person’s current state. Futhermore, transition speed is calculated in order to differentiate sit states from fall states. This study constructes a data set that includes over 1500 images, and the experimental evaluation of the images demonstrates that our enhanced algorithm is effective for detecting the falls with only a single camera.