# EE3P: Event-based Estimation of Periodic Phenomena Properties

arXiv: https://arxiv.org/abs/2402.14958 · HTML: https://mlsanity.com/paper/2402.14958/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [High Speed Rotation Estimation with Dynamic Vision Sensors](https://mlsanity.com/paper/2209.02205/) (2022)

## Built on by (most cited first, 2 of 2)

- [EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D](https://mlsanity.com/paper/2408.06899/) (2024)
- [Fourier-based Action Recognition for Wildlife Behavior Quantification with Event Cameras](https://mlsanity.com/paper/2410.06698/) (2024)

## Abstract (arXiv)

We introduce a novel method for measuring properties of periodic phenomena with an event camera, a device asynchronously reporting brightness changes at independently operating pixels. The approach assumes that for fast periodic phenomena, in any spatial window where it occurs, a very similar set of events is generated at the time difference corresponding to the frequency of the motion. To estimate the frequency, we compute correlations of spatio-temporal windows in the event space. The period is calculated from the time differences between the peaks of the correlation responses. The method is contactless, eliminating the need for markers, and does not need distinguishable landmarks. We evaluate the proposed method on three instances of periodic phenomena: (i) light flashes, (ii) vibration, and (iii) rotational speed. In all experiments, our method achieves a relative error lower than 0.04%, which is within the error margin of ground truth measurements.