Research on Multicamera Photography Image Art in BERT Motion Based on Deep Learning Mode

Abstract

In order to improve the artistic expression effect of photographic images, this article combines the deep learning model to conduct multicamera photographic image art research in BERT motion. Moreover, this article analyzes the external parameter errors caused in the calibration process and uses the checkerboard in the common field of view to calibrate the spatial coordinates of the corners of the board in multiple camera coordinate systems. In addition, this article aims to match the spatial coordinates of the corresponding points to each other and solve the rotation and translation matrix in the transformation process. Finally, this article uses the LM algorithm to optimize the calibration parameters of the camera and combines the deep learning algorithm to perform image processing. The experimental research results show that the research method of multicamera photography image art in BERT motion based on the deep learning mode proposed in this article can effectively improve the expression effect of image art.