Unobtrusive Fall Detection at Home Using Kinect Sensor

The existing CCD-camera based systems for fall detection require time for installation and camera calibration. They do not preserve the privacy adequately and are unable to operate in low lighting conditions. In this paper we show how to achieve automatic fall detection using only depth images. The point cloud corresponding to floor is delineated automatically using v-disparity images and Hough transform. The ground plane is extracted by the RANSAC algorithm. The detection of the person takes place on the basis of the updated on-line depth reference images. Fall detection is achieved using a classifier trained on features representing the extracted person both in depth images and in point clouds. All fall events were recognized correctly on an image set consisting of 312 images of which 110 contained the human falls. The images were acquired by two Kinect sensors placed at two different locations.