EV-LayerSegNet: Self-supervised Motion Segmentation using Event-based Cameras
Y. Farah (TU Delft - Aerospace Engineering)
G. C. H. E. de Croon – Mentor (TU Delft - Control & Simulation)
Erwin Mooij – Coach (TU Delft - Astrodynamics & Space Missions)
J Ellerbroek – Coach (TU Delft - Control & Simulation)
F. Paredes Valles – Graduation committee member (Sony)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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.