Computer vision is an area that has been growing continuously. With the advance of technologies with a first-person view, new development opportunities have emerged inside the area. Mixed reality promotes virtual environments with objects from the physical world shown in real time. For that, it's necessary to be concerned with the immersion of the user in this simulated environment, increasingly seeking to bring it closer to a possible desired reality. This paper proposes the development of image processing in order to perform the segmentation of images to identify what is foreground and background in order to facilitate the union of virtual and real images. Thus, the present work obtain real images of the user using the off-highway truck simulator CAT793F, through a camera, to be able to perform the segmentation of such images with artificial intelligence techniques.The convolutional neural network architectures "U-net" and "DeepLabV3+" are applied to perform image segmentation. As a result, metrics with around 90% accuracy were presented and and the best model was determined.

翻译：计算机视觉是一个持续发展的领域。随着第一人称视角技术的进步，该领域内涌现出新的发展机遇。混合现实技术通过实时融合物理世界中的虚拟环境与实体对象，需要重点关注用户在模拟环境中的沉浸感，并持续优化使其更贴近预期现实。本文提出通过图像处理实现前景与背景的语义分割，以促进虚拟与真实图像的融合。研究基于CAT793F非公路自卸卡车模拟器，通过摄像头采集用户真实图像，并应用"U-net"和"DeepLabV3+"两种卷积神经网络架构进行图像分割。实验结果显示，模型准确率可达90%左右，并确定了最优模型方案。