3D Privacy Masking using Monocular Depth Estimation
This thesis strives to dive deeper within the area of Monocular Depth Estimation, approximating distance information from one single image using deep neural networks. It introduces a thorough evaluation and analysis of state-of-the-art depth estimation models regarding proposed aspects of relevance for downstream video applications, specifically in a surveillance domain. This leads to three custom
