# Real-time Motion Segmentation with Event-based Normal Flow Source: [Real-time Motion Segmentation with Event-based Normal Flow]() Collection index: [llms.txt]() Data updated: 2026-02-26T05:00:04.574Z Authors: Sheng Zhong, Zhongyang Ren, Xiya Zhu, Dehao Yuan, Cornelia Fermuller, Yi Zhou Published: 2026-02-24T11:29:07.000Z ## Abstract Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle visual tasks in challenging scenarios. However, due to the sparse information content in individual events, directly processing the raw event data to solve vision tasks is highly inefficient, which severely limits the applicability of state-of-the-art methods in real-time tasks, such as motion segmentation, a fundamental task for dynamic scene understanding. Incorporating normal flow as an intermediate representation to compress motion information from event clusters within a localized region provides a more effective solution. In this work, we propose a normal flow-based motion segmentation framework for event-based vision. Leveraging the dense normal flow directly learned from event neighborhoods as input, we formulate the motion segmentation task as an energy minimization problem solved via graph cuts, and optimize it iteratively with normal flow clustering and motion model fitting. By using a normal flow-based motion model initialization and fitting method, the proposed system is able to efficiently estimate the motion models of independently moving objects with only a limited number of candidate models, which significantly reduces the computational complexity and ensures real-time performance, achieving nearly a 800x speedup in comparison to the open-source state-of-the-art method. Extensive evaluations on multiple public datasets fully demonstrate the accuracy and efficiency of our framework. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Motion Segmentation on EVIMO](): Motion Segmentation; EVIMO; mIoU (Box): 56. - [Independent Motion Object Detection on Extreme Event Dataset (EED)](): Independent Motion Object Detection; Extreme Event Dataset (EED); Performance (Fast Drone): 0.963. ## Original sources and implementation [Original paper]() [PDF]()