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

We present a novel method for measuring the period of phenomena like 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 the mean relative error of 0.1%. The dataset and codes are publicly available on GitHub.

EE3P3D: Event-based Estimation of Periodic Phenomena Frequency using 3D Correlation

We present a novel method for measuring the frequency 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 specific spatio-temporal window at a time difference corresponding to the phenomenon's period. The sets of similar events are detected by 3D spatio-temporal correlation in the event stream space. The proposed method, EE3P3D, 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). EE3P3D significantly outperforms published methods on this dataset, achieving a mean relative error of 0.1%.

EE3P: Event-based Estimation of Periodic Phenomena Properties

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.