Human Body Three-Dimensional Motion Skeleton Reconstruction of Moving Image Sequence

In the field of computer vision, the research of human motion analysis has a wide range of application prospects. Due to the complexity of human motion, the existing research methods have imposed many restrictions on the human body of the research object. A new method is proposed in this paper. The information of various types of human motion mainly discusses the reconstruction part of the human body's three-dimensional motion skeleton. The basic idea is to establish the image of each image based on the camera calibration and the application of three-dimensional human body model knowledge and motion continuity. On the basis of obtaining the binarized motion image, automatic annotation of joint feature points is realized. Firstly, the Canny operator is used to extract the target contour, and the Douglas-Peucker vector compression algorithm is used to obtain a suitable human contour shape. According to the joint proportional structure, the position of each joint point is determined, and the joint feature points are automatically labeled. The three-dimensional human motion tracking technology is studied. The characteristic optical flow algorithm is used to track the marked joint points, and the Kalman filter is used to correct the joint points of the tracking error. The automatic labeling method of the mannequin joint points used reduces the manual intervention in the system to some extent.