Real-time Unsupervised Object Discovery from Asynchronous Event Streams

2026-08-27

AI Summary

本文提出了一种轻量级无训练框架，通过时空聚类方法从异步事件流中实时发现移动物体，解决了延迟关键环境中的视觉感知问题。

Abstract

Event cameras capture pixel-level intensity changes with microsecond resolution to produce highly sparse asynchronous data streams. For visual perception in latency-critical environments, we propose a lightweight, training-free framework for discovery of moving objects based on spatio-temporal clustering. This framework is driven by two core contributions. First, a linear-time Spatio-temporal Probabilistic Event Filter (SPEF) that introduces an adaptive event acceptance threshold to distinguish salient motion structures from background noise. Second, an Event Morton Code Clustering (EMCC) module that bypasses expensive distance matrix computation to efficiently group events for unsupervised discovery of moving objects. On the E-MLB dataset benchmark, SPEF achieves the best denoising performance among classical filtering methods and remains competitive with learning-based approaches without requiring any offline training. On object discovery, EMCC achieves the highest overall accuracy and lowest execution time across the FRED and eTraM datasets, outperforming established density-based clustering baselines by a substantial margin. Overall, this work establishes a new performance benchmark for classical object discovery in event data, providing a highly scalable, training-free solution for resource-constrained visual perception. The code is available at https://github.com/PrathamShenwai/SPEF_EMCC

Problem

• event camera
• asynchronous data stream
• real-time
• unsupervised object discovery

Innovation

• Spatio-temporal Probabilistic Event Filter (SPEF)
• Event Morton Code Clustering (EMCC)
• unsupervised object discovery
• asynchronous event streams
• real-time