# 0-MMS: Zero-Shot Multi-Motion Segmentation With A Monocular Event Camera

arXiv: https://arxiv.org/abs/2006.06158 · HTML: https://mlsanity.com/paper/2006.06158/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting](https://mlsanity.com/paper/2111.03483/) (2021)
- [Motion Segmentation for Neuromorphic Aerial Surveillance](https://mlsanity.com/paper/2405.15209/) (2024)
- [Retina-Inspired Object Motion Segmentation for Event-Cameras](https://mlsanity.com/paper/2408.09454/) (2024)
- [Real-time Motion Segmentation with Event-based Normal Flow](https://mlsanity.com/paper/2602.20790/) (2026)

## Abstract (arXiv)

Segmentation of moving objects in dynamic scenes is a key process in scene understanding for navigation tasks. Classical cameras suffer from motion blur in such scenarios rendering them effete. On the contrary, event cameras, because of their high temporal resolution and lack of motion blur, are tailor-made for this problem. We present an approach for monocular multi-motion segmentation, which combines bottom-up feature tracking and top-down motion compensation into a unified pipeline, which is the first of its kind to our knowledge. Using the events within a time-interval, our method segments the scene into multiple motions by splitting and merging. We further speed up our method by using the concept of motion propagation and cluster keyslices. The approach was successfully evaluated on both challenging real-world and synthetic scenarios from the EV-IMO, EED, and MOD datasets and outperformed the state-of-the-art detection rate by 12%, achieving a new state-of-the-art average detection rate of 81.06%, 94.2% and 82.35% on the aforementioned datasets. To enable further research and systematic evaluation of multi-motion segmentation, we present and open-source a new dataset/benchmark called MOD++, which includes challenging sequences and extensive data stratification in-terms of camera and object motion, velocity magnitudes, direction, and rotational speeds.