Discrete pointvv 3D reconstruction algorithm based human pose estimation

The traditional method of human pose estimation lacks depth label in depth image, which leads to the low generalization ability of model due to single pose. A novel method of human pose estimation based on discrete point 3D reconstruction algorithm is proposed. Several cameras are placed around each other, and the internal parameters of the camera are calibrated by the classical calibration method, and the external parameters of the camera group are obtained. According to the two-dimensional skeleton, the human body region model is established, and the contour is matched with the human body model, so that the contour region is segmented, and then the three-dimensional human body model is constructed. According to the principle of discrete point 3D reconstruction algorithm, principal component analysis is used to transform the original data, eliminate the redundant correlation between the original human body posture data, establish the human body posture model, obtain 10 individual voxel sets, and estimate the 3D information of each point in the voxel set. In the process of feature extraction, two-dimensional attitude prediction, two-dimensional fusion layer and final conversion, the human body attitude estimation is realized. Experimental results show that this method can reduce the influence of image shadow on image recognition, and the estimation results are more accurate.