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This paper introduces a lightweight, training-free framework for real-time unsupervised object discovery from asynchronous event streams, leveraging spatio-temporal clustering techniques. The framework features a Spatio-temporal Probabilistic Event Filter (SPEF) that effectively distinguishes salient motion from background noise, and an Event Morton Code Clustering (EMCC) module that optimizes event grouping without the computational burden of distance matrices. Experimental results on the E-MLB, FRED, and eTraM datasets demonstrate that this approach outperforms classical and learning-based methods in both denoising performance and object discovery accuracy, setting a new benchmark for efficiency in resource-constrained environments.
Achieving real-time object discovery without any training, this framework outperforms traditional methods while remaining computationally efficient.
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
