Event Driven Clustering Algorithm

2026/02/28 by David El-Chai Ben-Ezra, Adar Tal, Daniel Brisk
#cs.CV #cs.LG

paper · pdf

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 Θ(N), where N is the number of events. Furthermore, the runtime is independent of the sensor resolution, i.e., the number of pixels.

Citations

• Event Camera Tuning for Detection Applications
• Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras
• Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization
• eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-
• Probabilistic Approach for Detection of High-Frequency Periodic Signals using an Event Camera
• AEGNN: Asynchronous Event-based Graph Neural Networks
• Feedback control of event cameras
• FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras
• Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras
• Real-time clustering and multi-target tracking using event-based sensors
• HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object\n Classification

Related

• eCDT: Event Clustering for Simultaneous Feature Detection and Tracking-
• Real-time clustering and multi-target tracking using event-based sensors
• ASAP: Adaptive Scheme for Asynchronous Processing of Event-based Vision Algorithms
• Graph-based Asynchronous Event Processing for Rapid Object Recognition
• Unsupervised Automated Event Detection using an Iterative Clustering based Segmentation Approach