Event Driven Clustering Algorithm

David El-Chai Ben-Ezra; Adar Tal; Daniel Brisk

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arXiv · 2602.00115

Event Driven Clustering Algorithm

David El-Chai Ben-Ezra·Adar Tal·Daniel Brisk

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Abstract

This paper introduces a novel asynchronous, event-driven algorithm for real-time detection of small event clusters in event camera data. Similar to hierarchical agglomerative clustering methods, the proposed algorithm detects clusters based on their spatio-temporal proximity. However, it explicitly leverages the asynchronous structure of event camera data and employs a simple yet efficient decision mechanism, achieving a linear time complexity of $\Theta(N)$, where $N$ is the number of events. Furthermore, the runtime is independent of the sensor resolution, i.e., the number of pixels.

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David El-Chai Ben-Ezra, Adar Tal, Daniel Brisk. 2026-01-27. Event Driven Clustering Algorithm. https://arxiv.org/abs/2602.00115

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