EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D
<|reference_start|>EEPPR: Event-based Estimation of Periodic Phenomena Rate using Correlation in 3D: 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.<|reference_end|>
@article{kolář2024eeppr:,
    title={EEPPR: Event-based Estimation of Periodic Phenomena Rate using
  Correlation in 3D},
    author={Jakub Kol'av{r} and Radim v{S}petl'ik and Jiv{r}'i Matas},
    journal={arXiv preprint arXiv:2408.06899},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06899},
    primaryClass={cs.CV}
}