EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D

TL;DR

This paper introduces EEPPR, a new event-based method that accurately estimates the rate of periodic phenomena using correlation in event streams, outperforming existing techniques with high precision.

Contribution

The paper presents EEPPR, a novel correlation-based approach for estimating periodic phenomena rates from event camera data, achieving state-of-the-art accuracy.

Findings

Achieves a mean relative error of 0.1% on diverse datasets.

Successfully estimates frequencies from 3.2 Hz to 2 kHz.

Outperforms existing methods significantly.

Abstract

We present a novel method for measuring the rate of periodic phenomena (e.g., rotation, flicker, and vibration), by an event camera, a device asynchronously reporting brightness changes at independently operating pixels with high temporal resolution. The approach assumes that for a periodic phenomenon, a highly similar set of events is generated within a spatio-temporal window at a time difference corresponding to its period. The sets of similar events are detected by a correlation in the spatio-temporal event stream space. The proposed method, EEPPR, is evaluated on a dataset of 12 sequences of periodic phenomena, i.e. flashing light and vibration, and periodic motion, e.g., rotation, ranging from 3.2 Hz to 2 kHz (equivalent to 192 - 120 000 RPM). EEPPR significantly outperforms published methods on this dataset, achieving a mean relative error of 0.1%, setting new state-of-the-art. The dataset and codes are publicly available on GitHub.