Fall detection using Gaussian mixture model and principle component analysis

Fall accident whose rates increase exponentially is the major risk for the elderly, especially those living alone. A fall accident detection system to detect the fall accident and call for an emergency is essential for elderly. This paper proposes to extract human from a video camera using a mixture of Gaussian model combined with average filter models. The proposed method extracts six postures of physically movements of human including lying, sitting, standing, getting up, walking, and falling. Unique features such as inter-frames information, shape description from a silhouette aspect ratio, and orientation of principal component are obtained. The method could automatically alarm when the fall is detected. The experimental results show the detection rate up to 86.21% of the 58 videos from the Le2i dataset.