  Event cameras are a novel type of biologically inspired vision sensor known
for their high temporal resolution, high dynamic range, and low power
consumption. Because of these properties, they are well-suited for processing
fast motions that require rapid reactions. Although event cameras have recently
shown competitive performance in unsupervised optical flow estimation,
performance in detecting independently moving objects (IMOs) is lacking behind,
although event-based methods would be suited for this task based on their low
latency and HDR properties. Previous approaches to event-based IMO segmentation
have been heavily dependent on labeled data. However, biological vision systems
have developed the ability to avoid moving objects through daily tasks without
being given explicit labels. In this work, we propose the first event framework
that generates IMO pseudo-labels using geometric constraints. Due to its
unsupervised nature, our method can handle an arbitrary number of not
predetermined objects and is easily scalable to datasets where expensive IMO
labels are not readily available. We evaluate our approach on the EVIMO dataset
and show that it performs competitively with supervised methods, both
quantitatively and qualitatively.
