Network algorithm real-time depth image 3D human recognition for augmented reality

This paper studies the application of augmented reality real-time depth image technology to 3D human motion recognition technology. The accuracy and real-time performance of sensor-based 3D human reconstruction are affected by visual characteristics and illumination changes. Features are not easily extracted and cannot be tracked, leading to failures in the 3D reconstruction of the human body. Based on this system, the sensor-based visual inertial initialization algorithm is studied, which is integrated in the two-frame image time interval to provide accurate initial values for vision-based motion estimation, improve the accuracy of the calculated posture, and finally improve the accuracy of the 3D reconstruction system. Based on the relationship between the depth image and the distance and reflectivity, a model for correcting the distance error and reflectivity error of the depth image is established to improve the accuracy of the depth image, and finally the accuracy of the three-dimensional reconstruction of the human body.