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Looking into the Future: Predicting Future Video Frames Using Monocular Depth Estimation and Egomotion
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Looking into the Future: Predicting Future Video Frames Using Monocular Depth Estimation and Egomotion
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Dissertation
Looking into the Future: Predicting Future Video Frames Using Monocular Depth Estimation and Egomotion
2021
Overview
Micro-mobility has become a growing market that has altered transportation within cities. While helping people reach their destination efficiently while using fewer fossil fuels and resources, e-scooters lack tailored safety protocol. Using depth and ego-motion machine learning estimation through a live video stream, we hope to identify possible oncoming hazard for e-scooter users. To approach this problem, we tested methods that removed the pose network with a scaling transformation which was derived via linear regression. Our intuition was that training and inference will be faster with the removal of the pose network and was validated by the results. We also found that forward warping has good accuracy using the transformed ground truth ego motion over the relative ego motion from the pose network. This discovery can be used to predict if an object within the scene has a probability of colliding with the e-scooter user.
Publisher
ProQuest Dissertations & Theses
ISBN
9798209779537
