Depth Estimation of Computer Video Images Based on Deep Learning

Abstract

Image depth estimation is an important technology for obtaining scene depth for 3D images, and it has developed rapidly in the field of computer vision. In this paper, convolutional neural networks and conditional random fields are unified into a deep learning framework to build a computer vision model. First, based on the architecture of multiscale CNN and CRF, information of the scene image is obtained from computer video using depth learning, and then, a new frame model is built to predict the depth of images in a computer video. Second, three international standard datasets are used for training, and the results show that the CNN–CRF model can use a small number of samples to complete high-precision training and has a better estimation effect on images outside the dataset. The experimental results show that the CNN–CRF model can recover the depth and speculate the 3D structure of the scene, which has predictable application and development value for improving production efficiency.