Kolář, J., Špetlík, R., Matas, J. (2024) Measuring Speed of Periodical Movements with Event Camera. In Proceedings of the 27th Computer Vision Winter Workshop, 2024
Paper Link: arXiv, CVWW proceedings
Data Capture Demonstration: Video
- Data captured from an event camera is aggregated into non-overlapping arrays along the time axis,
- A region of Interest and a template are selected,
- 2D correlation of the template with arrays is computed,
- The frequency is calculated from the average of time deltas measured between correlation peaks.
The data is organized into folders, each representing a specific experiment from the paper. Each folder contains the following files:
.raw
: Raw event camera data in the EVT 3.0 format._slowdownfactor_fps.avi
: Slowed down video of the event stream._tachometer_data.xml
: Ground truth data from the laser tachometer (if applicable).
The files were compressed and split into multiple .rar
files to meet GitHub file size guidelines.
01_line
: Experiment measuring the rotational speed of a disc with a high-contrast mark.02_velcro
: Experiment measuring the rotational speed of a disc with a uniform velcro surface.03_velcroside
: Experiment measuring the rotational speed of a disc with velcro, viewed from the side.04_speaker
: Experiment measuring the vibration frequency of a speaker diaphragm.05_led
: Experiment measuring the flashing frequency of an LED.
Note: Not all experiments use the laser tachometer, hence the absence of the corresponding data file in some folders.
The data can be used for various purposes, including:
- Reproducing the experiments in the paper.
- Developing and testing new methods for frequency and rotational speed estimation using event cameras.
- Comparing the performance of different event camera-based methods.
The data is provided under the GPL-3.0 license. Please refer to the LICENSE file for details.
We encourage you to use this data responsibly and cite the paper if you use it in your work:
@INPROCEEDINGS{kolář_ee3p_2024,
title={EE3P: Event-based Estimation of Periodic Phenomena Properties},
booktitle={Proceedings of the 27th Computer Vision Winter Workshop 2024},
pages={66-74},
author={Jakub Kolář and Radim Špetlík and Jiří Matas},
year={2024},
eprint={2402.14958},
archivePrefix={arXiv},
primaryClass={cs.CV},
doi={10.48550/ARXIV.2402.14958}
}