Event Driven Clustering Algorithm

TL;DR

This paper presents an innovative, real-time, event-driven clustering algorithm for event camera data that achieves linear complexity and is independent of pixel array dimensions, enabling efficient small event cluster detection.

Contribution

The paper introduces a novel asynchronous clustering algorithm leveraging event camera data's unique structure, with linear complexity and pixel dimension independence.

Findings

Achieves linear complexity of O(n) for clustering.

Runs efficiently regardless of pixel array size.

Effective detection of small event clusters in real-time.

Abstract

This paper introduces a novel asynchronous, event-driven algorithm for real-time detection of small event clusters in event camera data. Like other hierarchical agglomerative clustering algorithms, the algorithm detects the event clusters based on their tempo-spatial distance. However, the algorithm leverages the special asynchronous data structure of event camera, and by a sophisticated, efficient and simple decision-making, enjoys a linear complexity of $O(n)$ where $n$ is the events amount. In addition, the run-time of the algorithm is independent with the dimensions of the pixels array.