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

arXiv: https://arxiv.org/abs/2408.06899 · HTML: https://mlsanity.com/paper/2408.06899/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Frequency Cam: Imaging Periodic Signals in Real-Time](https://mlsanity.com/paper/2211.00198/) (2022)
- [Table tennis ball spin estimation with an event camera](https://mlsanity.com/paper/2404.09870/) (2024)
- [High Speed Rotation Estimation with Dynamic Vision Sensors](https://mlsanity.com/paper/2209.02205/) (2022)
- [EE3P: Event-based Estimation of Periodic Phenomena Properties](https://mlsanity.com/paper/2402.14958/) (2024)

## Built on by (most cited first, 3 of 3)

- [HelixTrack: Event-Based Tracking and RPM Estimation of Propeller-like Objects](https://mlsanity.com/paper/2603.09235/) (2026)
- [Relative State Estimation using Event-Based Propeller Sensing](https://mlsanity.com/paper/2604.18289/) (2026)
- [Event-Only Wingbeat Counting under Camera Motion: A Controlled MuJoCo Benchmark](https://mlsanity.com/paper/2609.37465/) (2026)

## Abstract (arXiv)

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.