基于异步事件流的实时无监督目标发现
Real-time Unsupervised Object Discovery from Asynchronous Event Streams
- School of Engineering and Technology（工程与技术学院）
- University of New South Wales（新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种轻量型、无需训练的异步事件流运动目标发现框架，含SPEF与EMCC两项核心组件，在多数据集上实现优异的去噪与目标发现性能，为资源受限视觉感知提供新方案。
AI中文摘要：
事件相机以微秒级分辨率捕获像素级强度变化，生成高度稀疏的异步数据流。针对低延迟环境下的视觉感知需求，本文提出一种基于时空聚类的轻量型、无需训练的运动目标发现框架，该框架包含两项核心贡献：一是线性时间复杂度的时空概率事件过滤器（SPEF），引入自适应事件接受阈值以区分显著运动结构与背景噪声；二是事件莫顿码聚类（EMCC）模块，绕开昂贵的距离矩阵计算，高效分组事件以实现运动目标的无监督发现。在E-MLB数据集基准测试中，SPEF在经典过滤方法中实现最佳去噪性能，且无需任何离线训练即可与基于学习的方法保持竞争力；在目标发现任务中，EMCC在FRED和eTraM数据集上实现最高整体准确率与最低执行时间，大幅优于现有基于密度的聚类基线方法。总体而言，本研究为事件数据中的经典目标发现建立了新的性能基准，为资源受限的视觉感知提供了高度可扩展、无需训练的解决方案，代码可通过指定URL获取。
英文摘要：
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
