# A Multimodal Eye Movement Dataset and a Multimodal Eye Movement Segmentation Analysis

arXiv: https://arxiv.org/abs/2101.04318 · HTML: https://mlsanity.com/paper/2101.04318/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 79 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities](https://mlsanity.com/paper/1905.13146/) (2019)
- [EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking](https://mlsanity.com/paper/2007.09600/) (2020)
- [PupilNet: Convolutional Neural Networks for Robust Pupil Detection](https://mlsanity.com/paper/1601.04902/) (2016)
- [ElSe: Ellipse Selection for Robust Pupil Detection in Real-World Environments](https://mlsanity.com/paper/1511.06575/) (2015)
- [Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction](https://mlsanity.com/paper/2002.10905/) (2020)
- [Rotated Ring, Radial and Depth Wise Separable Radial Convolutions](https://mlsanity.com/paper/2010.00873/) (2020)
- [Eye movement velocity and gaze data generator for evaluation, robustness testing and assess of eye tracking software and visualization tools](https://mlsanity.com/paper/1808.09296/) (2018)
- [Learning to Validate the Quality of Detected Landmarks](https://mlsanity.com/paper/1901.10143/) (2019)
- [Weight and Gradient Centralization in Deep Neural Networks](https://mlsanity.com/paper/2010.00866/) (2020)
- [Fast camera focus estimation for gaze-based focus control](https://mlsanity.com/paper/1711.03306/) (2017)
- [Eye movement simulation and detector creation to reduce laborious parameter adjustments](https://mlsanity.com/paper/1804.00970/) (2018)
- [Training Decision Trees as Replacement for Convolution Layers](https://mlsanity.com/paper/1905.10073/) (2019)
- [Multi Layer Neural Networks as Replacement for Pooling Operations](https://mlsanity.com/paper/2006.06969/) (2020)
- [Explainable Online Validation of Machine Learning Models for Practical Applications](https://mlsanity.com/paper/2010.00821/) (2020)
- [End-to-End Eye Movement Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1609.02452/) (2016)

## Built on by (most cited first, 7 of 7)

- [TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types](https://mlsanity.com/paper/2102.02115/) (2021)
- [1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning](https://mlsanity.com/paper/2102.01921/) (2021)
- [Maximum and Leaky Maximum Propagation](https://mlsanity.com/paper/2105.10277/) (2021)
- [Tensor Normalization and Full Distribution Training](https://mlsanity.com/paper/2109.02345/) (2021)
- [GroupGazer: A Tool to Compute the Gaze per Participant in Groups with integrated Calibration to Map the Gaze Online to a Screen or Beamer Projection](https://mlsanity.com/paper/2201.07692/) (2022)
- [Technical Report: Combining knowledge from Transfer Learning during training and Wide Resnets](https://mlsanity.com/paper/2206.09697/) (2022)
- [A temporally quantized distribution of pupil diameters as a new feature for cognitive load classification](https://mlsanity.com/paper/2303.12757/) (2023)

## Abstract (arXiv)

We present a new dataset with annotated eye movements. The dataset consists of over 800,000 gaze points recorded during a car ride in the real world and in the simulator. In total, the eye movements of 19 subjects were annotated. In this dataset there are several data sources such as the eyelid closure, the pupil center, the optical vector, and a vector into the pupil center starting from the center of the eye corners. These different data sources are analyzed and evaluated individually as well as in combination with respect to their goodness of fit for eye movement classification. These results will help developers of real-time systems and algorithms to find the best data sources for their application. Also, new algorithms can be trained and evaluated on this data set. The data and the Matlab code can be downloaded here https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FA%20Multimodal%20Eye%20Movement%20Dataset%20and%20...&mode=list