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

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