Iterative Event-based Motion Segmentation by Variational Contrast Maximization
Journal:
arXiv
Published Date:
Apr 25, 2025
Abstract
Event cameras provide rich signals that are suitable for motion estimation
since they respond to changes in the scene. As any visual changes in the scene
produce event data, it is paramount to classify the data into different motions
(i.e., motion segmentation), which is useful for various tasks such as object
detection and visual servoing. We propose an iterative motion segmentation
method, by classifying events into background (e.g., dominant motion
hypothesis) and foreground (independent motion residuals), thus extending the
Contrast Maximization framework. Experimental results demonstrate that the
proposed method successfully classifies event clusters both for public and
self-recorded datasets, producing sharp, motion-compensated edge-like images.
The proposed method achieves state-of-the-art accuracy on moving object
detection benchmarks with an improvement of over 30%, and demonstrates its
possibility of applying to more complex and noisy real-world scenes. We hope
this work broadens the sensitivity of Contrast Maximization with respect to
both motion parameters and input events, thus contributing to theoretical
advancements in event-based motion segmentation estimation.
https://github.com/aoki-media-lab/event_based_segmentation_vcmax
