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

• URL: http://arxiv.org/abs/2408.06899v3
• Date: Sun, 15 Sep 2024 14:16:47 GMT
• Title: EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D
• Authors: Jakub Kolář, Radim Špetlík, Jiří Matas,
• Abstract summary: We present a novel method for measuring the rate of periodic phenomena by an event camera. The approach assumes that for a periodic phenomenon, a highly similar set of events is generated within atemporal window at a time difference to its period space. The proposed method, EEPEP, is evaluated on a dataset of 12 sequences of periodic phenomena.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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