Improving depth estimation in an automated privacy-preserving video processing system

Abstract

This master thesis is written in collaboration with Info Support B.V. for one of their clients, and explores the use of a conditional Generative Adversarial Network for the estimation of depth from monocular rgb images. The main research question stated is: Can a state-of-the-art neural network be trained to accurately estimate relative depth in a crowded scene from monocular recordings of a single uncalibrated camera? This question was answered through three sub-questions, which addressed the addition of biological cues, temporal information, and transfer learning from hyper-realistic video game data. The model trained was improved through addition of biological cues, temporal information and transfer learning, but was not accurate enough for the intended application of depth estimation from monocular recordings in a crowded scene.