Recognition of Human Walking Motion Using a Wearable Camera

In recent years, the computer vision technology has been attracting more attention than ever and being applied in a wide range of fields. Among them, the technology on automatic recognition of human motion is particularly important, since it leads to automatic detection of suspicious persons and automatic monitoring of elderly people. Therefore, the research on human motion recognition using computer vision techniques has been actively conducted in Japan and overseas. However, most of the conventional researches on human motion recognition employs a video of a human motion taken using an external fixed camera. There is no research on human motion recognition using a video of a surrounding scenery provided from a wearable camera. This paper proposes a method of recognizing a human motion by estimating the posture change of a wearable camera attached to a walking human from the motion of a scenery in the video provided from the wearable camera and by analyzing a human trunk change obtained from the posture change of the camera. In the method, AKAZE is applied to the images to detect feature points and to find their correspondence. The 5-point algorithm is used to estimate the Epipolar geometry constraint and an essential matrix which provides a camera relative motion. The change of the camera relative motion is then used to analyze the shape of a human trunk. The analyzed results, i.e., walking motion features, are finally fed into a SVM to identify the motion. In the experiment, five types of walking motions are captured by a wearable camera from five subjects. The accuracy on human motion recognition was 80%. More precise feature points extraction, more exact estimation of motions, and considering variety of human walking motions are needed to improve the proposed technique.