A novel real-time fall detection method based on head segmentation and convolutional neural network

As the computer vision develops, real-time fall detection based on computer vision has become increasingly popular in recent years. In this paper, a novel real-time indoor fall detection method based on computer vision by using geometric features and convolutional neural network (CNN) is proposed. Gaussian mixture model (GMM) is applied to detect the human target and find out the minimum external elliptical contour. Differently from the traditional fall detection method based on geometric features, we consider the importance of the head in fall detection and propose to use two different ellipses to represent the head and the torso, respectively. Three features including the long and short axis ratio, the orientation angle and the vertical velocity are extracted from the two different ellipses in each frame, respectively, and fused into a motion feature based on time series. In addition, a shallow CNN is applied to find out the correlation between the two elliptic contour features for detecting indoor falls and distinguishing some similar activities. Our novel method can effectively distinguish some similar activities in real time, which cannot be distinguished by some traditional methods based on geometric features, and has a better detection rate.