Application of Gesture Estimation Method Based on Computer

Abstract

This paper will study the computer vision pose estimation algorithm and apply it to fall detection. At present, the problem of aging cannot be ignored. There are more and more elderly people living alone. Due to their poor physical fitness, they are very easy to fall in daily life and bring great harm to the body and mind of the elderly. Therefore, they are in great need of means to monitor the daily life of the elderly in real time and make judgments on fall events. Driven by the rapid development of computer vision technology, human pose estimation has been widely used in fields such as motion recognition, motion posture scoring, and assisted rehabilitation therapy. The core of pose estimation is to detect the joint information and bone information of the human body from the image, and connect them to form a bone image to help the computer understand the human body information. The use of pose estimation for fall detection requires the extraction of human joint point information and bone information. In order to extract human joint point coordinates and bone images from video sequences, this paper has done a lot of research on computer vision pose estimation. First, the background subtraction method is studied. After that, the openpose pose estimation algorithm is studied. then studied the attitude estimation of Azure Kinect DK depth camera, focusing on its TOF principle and body tracking principle. After comparison, it is found that traditional pose estimation such as openpose is based on optical RGB images. In this paper, we mainly use Azure Kinect DK depth camera for fall detection. In the fall detection method, this paper uses the threshold method combined with pattern recognition method to detect falls. The thresholding method uses the head fall speed and the body tilt angle. The tilt angle of the upper body is used to solve the bending and squatting errors. The pattern recognition method transforms fall detection into two classification problems, fall and no-fall, and uses a support vector machine binary classification method combined with a directed gradient histogram to extract features from skeletal images for classification. The final experimental data show that this paper implements fall detection based on human posture estimation using Azure Kinect DK depth camera with high accuracy and practicality.