EE3P: Event-based Estimation of Periodic Phenomena Properties

TL;DR

EE3P introduces a non-contact, event-camera-based method to estimate properties of periodic phenomena by aggregating events in a Region of Interest, correlating with a template, and extracting the period from peaks in the correlation responses. The approach yields frequency or rotation-speed estimates from the inter-peak deltas with formulas such as $\nu_i = \frac{10^{6}}{\Delta t_i}$ or $\text{RPM}_i = \frac{10^{6}}{\Delta t_i} \times 60$, and averages over samples for robustness. Across light flashes, vibrations, and rotations, the method attains relative errors $< \pm 0.04\%$, demonstrates resilience to camera angle and even transparent viewing through glass, and is applicable without markers or landmarks. The authors release a public dataset and provide practical guidance on RoI selection, aggregation duration, and template design, highlighting EE3P’s potential for markerless, high-temporal-resolution frequency estimation in industrial and research contexts.

Abstract

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.

Figures (6)

• Figure 1: The proposed method: (i) data captured from an event camera is aggregated into non-overlapping arrays along the time axis, (ii) a Region of Interest and a template are selected, (iii) D correlation of the template with arrays is computed, (iv) and the frequency is calculated from the average of time deltas measured between correlation peaks.
• Figure 2: Experimental setup with visualisation of event camera output. Top: physical setups, bottom: events from a 250-millisecond window visualised in spatio-temporal space.
• Figure 3: Aggregated events in a fixed time interval of one millisecond for a selected Region of Interest. Positive events are represented by white color, and negative events are bright blue.
• Figure 7: The fronto-parallel felt disc with a high-contrast mark experiment (see Fig. \ref{['fig:physical_setups']}c).
• Figure 9: The fronto-parallel velcro disc experiment (see Fig. \ref{['fig:physical_setups']}d).