Fall detection in the elderly by head tracking

In the paper, we propose a fall detection method based on head tracking within a smart home environment equipped with video cameras. A motion history image and code-book background subtraction are combined to determine whether large movement occurs within the scene. Based on the magnitude of the movement information, particle filters with different state models are used to track the head. The head tracking procedure is performed in two video streams taken by two separate cameras and three-dimensional head position is calculated based on the tracking results. Finally, the three-dimensional horizontal and vertical velocities of the head are used to detect the occurrence of a fall. The success of the method is confirmed on real video sequences.