EV-LayerSegNet: Self-supervised Motion Segmentation using Event-based Cameras

Master thesis (2023)

Authors

Y. Farah Aerospace Engineering

Contributors

G.C.H.E. de Croon (supervisor 1)
E. Mooij Astrodynamics & Space Missions - Aerospace Engineering (coach)
F. Paredes Valles ExternalOrganization (supervisor 2)
Faculty
Aerospace Engineering
Copyright: © 2023 Youssef Farah
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Published Date

13-07-2023

Language

English

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Faculty

Aerospace Engineering

Abstract

Event cameras are novel bio-inspired sensors that capture motion dynamics with much higher temporal resolution than traditional cameras, since pixels react asynchronously to brightness changes. They are therefore better suited for tasks involving motion such as motion segmentation. However, training event-based networks still represents a difficult challenge, as obtaining ground truth is very expensive and error-prone. In this article, we introduce EV-LayerSegNet, the first self-supervised CNN for event-based motion segmentation. Inspired by a layered representation of the scene dynamics, we show that it is possible to learn affine optical flow and segmentation masks separately, and use them to deblur the input events. The deblurring quality is then measured and used as self-supervised learning loss.