Research And Application Of Pose Estimation Method Based On Computer Vision

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.Therefore,this paper will study the computer vision pose estimation algorithm and apply it to fall detection.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.For moving target detection,the algorithm can find the moving human body from the image,and can preprocess the data.Only the target area is studied to provide a basis for pose estimation.After that,the openpose pose estimation algorithm is studied,focusing on its core idea PAF(Part Affinity Fields)and joint point confidence,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 posture estimation such as openpose is based on optical RGB images.Optical images will have different results under the influence of light,and they cannot be used at night.Azure Kinect DK depth camera solves the problem of light due to the use of depth information.The problem is that real-time monitoring can be achieved throughout the day,and the obtained bone images do not contain environmental information,only bone information is retained,which plays a role in protecting privacy.Therefore,this paper mainly uses Azure Kinect DK depth camera for fall detection.In the fall detection method,this paper uses the threshold method combined with the pattern recognition method to detect falls.The threshold method uses the head descent speed and the body inclination angle.The upper body inclination angle is used to solve the mistakes of bending and squatting.The pattern recognition method converts fall detection into two classification problems of fall and not-fall,and uses the support vector machine two classification method combined with the histogram of oriented gradient to classify the bone image extraction features.The final experimental data shows that this paper uses Azure Kinect DK depth camera to achieve fall detection based on human pose estimation,which has high accuracy and practicability.