Improving depth estimation in an automated privacy-preserving video processing system
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Issue Date
2020-04-01
Language
en
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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.
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Faculteit der Sociale Wetenschappen