Event Camera Benchmark Dataset

This is the dataset for the paper

Experimental Comparison between Event and Global Shutter Cameras.
Holešovský, O., Škoviera, R., Hlaváč, V., Vítek, R. (2021).
Sensors (Basel, Switzerland), 21. https://doi.org/10.3390/s21041137

If you use any of this data in an academic context, please cite the following publication:

author = {Holešovský, Ondřej and Škoviera, Radoslav and Hlaváč, Václav and Vítek, Roman},
title = {Experimental Comparison between Event and Global Shutter Cameras},
journal = {Sensors},
volume = {21},
year = {2021},
number = {4},
article-number = {1137},
url = {https://www.mdpi.com/1424-8220/21/4/1137},
issn = {1424-8220},
doi = {10.3390/s21041137}

Sample Python scripts

We provide two Python scripts which demonstrate how to read the dataset. Before executing any of the scripts, set the value of the global variable hdf5_root in the header of the script to the path of the directory containing the downloaded dataset or its parts (hdf5 files and/or unpacked tar/zip archives, see below).

python sample_dot.py loads all rotating dot datasets available in hdf5_root recorded by each camera (ATIS, DVS240, Basler). For each camera available, it calibrates the encoder-camera position ground truth and shows a sample event/frame image of the dot with the ground truth position marked by a green cross.

python sample_ballistic.py loads the ballistic dataset (ballistic.hdf5). It shows a sample image of each of the three included projectile types. The sample image displays ATIS events and a Photron intensity frame, both transformed into the same metric world coordinates using the camera calibration parameters stored in the dataset.

We tested the scripts on a GNU/Linux platform with these software dependencies:

ffmpeg 4.1.3
imageio 2.5.0
imageio-ffmpeg 0.3.0
h5py 2.10.0
matplotlib 3.1.0
numpy 1.17.4
opencv 4.1.0
python 3.7.3
scipy 1.6.1

Dataset files

In the table below, choose and download the dataset components you wish to use and place (or extract) them all in the same root directory (called hdf5_root in the sample Python scripts). The two event cameras are ATIS and DVS240, the frame cameras are Basler and Photron.

File Size Description
aruco_atis.hdf5 3.8 GiB Rotating ArUco markers. ATIS.
aruco_dvs240.hdf5 1.8 GiB Rotating ArUco markers. DVS240.
aruco_basler.tar 1.9 GiB Rotating ArUco markers. Basler.
dot_atis.hdf5 765 MiB Rotating dots. ATIS.
dot_dvs240.hdf5 235 MiB Rotating dots. DVS240.
dot_basler.tar 2.0 GiB Rotating dots. Basler.
ballistic.hdf5 512 MiB All ballistic recordings including calibration parameters and images.
ballistic_calib_images.zip 6.0 MiB Ballistic camera calibration images as png files.
atis_dvs240_config.zip 8 KiB Bias configuration files of the ATIS and DVS240 event cameras.
sample_dot.py 10 KiB Demo script: show rotating dot images or events.
sample_ballistic.py 5 KiB Demo script: show ballistic projectile images and events.
LICENSE.txt 1 KiB The software license for the two Python scripts (MIT License).
sha256sums.txt 1 KiB Checksums (sha256sum) of all the files above.

In addition to trying the sample Python scripts above, you can use HDFView to browse the HDF5 files. The most recent 3.1.2 version we tested worked well, similarly to version 2.9 (from Ubuntu 16.04 package repositories). However, version 2.11 (in 18.04 and 20.04 Ubuntu repos) could not load the files properly.


This dataset is released under the Creative Commons license (CC BY-NC-SA 4.0), Attribution-NonCommercial-ShareAlike, which is free for non-commercial use (including research).

Rotating disk

HDF5 tree structure



Ballistic experiments

The ATIS camera used biases biases/ATIS_gen3_EM_faster_s40.bias with sensitivity 40 in all the ballistic recordings.

HDF5 tree structure



Camera calibration

We calibrated the cameras in Matlab 2020a Camera Calibrator using the images of chessboard patterns stored in ballistic/calibration/[atis or photron]/image_sequence. Both stationary cameras simultaneously captured images of the chessboard at 17 different positions. Therefore these 17 image pairs can also be used for calibrating a Photron-ATIS stereo camera.

The Photron calibration images are 1024 x 1024 pixels in size, whereas the Photron ballistic recordings are just 640 x 280. The smaller images are simply center crops from the full 1024 x 1024 pixel Photron image.

The sample Python script demonstrates how to transform ATIS events and Photron images of a flying projectile to the common world plane using the calibration parameters stored in the HDF5 (see the attributes of calibration/atis or calibration/photron above). The 3D rotation and translation determine the position of each camera with respect to the first calibration chessboard plane (the first image in the calibration image_sequence).

The transformation between world coordinates (X,Y,Z) and undistorted image coordinates (x,y) is

x & y & 1
\end{array}] =
X & Y & Z & 1

where K is the 3x3 camera_matrix

f_x & 0 & c_x\\
0 & f_y & c_y\\
0 & 0 & 1

R is the 3x3 rotation matrix, t is the 1x3 translation vector and s a scale factor, which can be fixed by setting Z=0 (the depth coordinate of the calibration plane). c_x, c_y are the pixel coordinates of the optical center; f_x, f_y is the focal length expressed in horizontal and vertical pixel units.