Event Driven Clustering Algorithm

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.

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2602.00115 [cs.CV]

(or arXiv:2602.00115v2 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2602.00115 Focus to learn more arXiv-issued DOI via DataCite

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