LPW
2D Polygon
Eye
|...
许可协议: Custom

Overview

This is a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people with different ethnicities, a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, as well as make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution, vision aids, as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers.

Data Collection

Participants

Detailed information about our participants can be found in Table 2. We recruited 22 participants including 9 female through university mailing lists and personal communication. Among them are five different ethnicities: 11 Indian, 6 German, 2 Pakistani, 2 Iranian, and 1 Egyptian. In total we had five different eye colors: 12 brown, 5 black, 3 blue-gray, 1 blue-green, 1 green. Also 5 people had impaired vision, 2 wore glasses and 1 wore contact lenses. Strong eye make-up was worn by 1 person (with participant ID 22).

Apparatus

The eye tracker used for the recording was a high-speed Pupil Pro head-mounted eye tracker that record eye videos with 120 Hz [Kass- ner et al. 2014]. In order to capture high frame rate scene videos, we replaced the original scene camera with a PointGrey Chameleon3 USB3.0 camera recording at up to 149 Hz. The hardware set up is shown in Figure 2a and Figure 2b. It allowed us to record all videos with 95 FPS, which is a speed at which even fast eye movements last through several frames.

Procedure

As shown in the right image below, the participants were instructed to look at a moving red ball as a fixation target during the data collection. The position of the red ball in the visual field of the participant is shown in middle image below with an image captured by the scene camera. In order to cover as many different conditions as possible, we randomly picked the recording locations in and around of several buildings. Each location was not chosen more than once during the whole recording of all participants. 34.3% of the recordings were done outdoors, in 84.7% natural light was present and in 33.6% artificial light was present. Besides locations, we have also tweaked the angle of the eye cameras such that the dataset contains a wide range of camera angles from frontal views to highly off-axis angles. This is done by either asking the participant to take the tracker off and put it back on, or manually moving the camera. With each of the 22 participant we recorded three videos with around 20 seconds length, yielding 130,856 images overall.Participants could keep their glasses and contact lenses on during the recording.

Data Annotation

We used different methods for annotation. In many easy cases such as some indoor recordings, the pupil area has a clear boundary and no strong reflections inside. We annotated these frames by manually selecting 1 or 2 points inside the pupil area, using them as seed points to find the largest connected area with similar intensity values. The pupil center is defined as the centroid of this area. Some recordings have a clear scene video but strong reflections/noise in the eye video, such as outdoor recordings under strong sunlight. In those cases, we tracked the fixation target (red ball) in the scene videos and manually annotated part of the eye pupil positions in the eye videos. From this calibration data we com- puted a mapping function from target positions to pupil positions. In addition, we examined the annotated videos again to find wrong annotations, and corrected them by selecting 5 or more points on the pupil boundary and fitting an ellipse to them. The center of the ellipse was used as a refined pupil center position.

Citation

@inproceedings{tonsen2016labelled,
  title={Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained
environments},
  author={Tonsen, Marc and Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas},
  booktitle={Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking
Research \& Applications},
  pages={139--142},
  year={2016}
}

License

Custom

数据概要
数据格式
Video,
数据量
--
文件大小
2.4GB
发布方
Max Planck Institute for Informatics, Saarbrucken, Germany
数据集反馈
| 92 | 数据量 -- | 大小 2.4GB
LPW
2D Polygon
Eye
许可协议: Custom

Overview

This is a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people with different ethnicities, a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, as well as make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution, vision aids, as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers.

Data Collection

Participants

Detailed information about our participants can be found in Table 2. We recruited 22 participants including 9 female through university mailing lists and personal communication. Among them are five different ethnicities: 11 Indian, 6 German, 2 Pakistani, 2 Iranian, and 1 Egyptian. In total we had five different eye colors: 12 brown, 5 black, 3 blue-gray, 1 blue-green, 1 green. Also 5 people had impaired vision, 2 wore glasses and 1 wore contact lenses. Strong eye make-up was worn by 1 person (with participant ID 22).

Apparatus

The eye tracker used for the recording was a high-speed Pupil Pro head-mounted eye tracker that record eye videos with 120 Hz [Kass- ner et al. 2014]. In order to capture high frame rate scene videos, we replaced the original scene camera with a PointGrey Chameleon3 USB3.0 camera recording at up to 149 Hz. The hardware set up is shown in Figure 2a and Figure 2b. It allowed us to record all videos with 95 FPS, which is a speed at which even fast eye movements last through several frames.

Procedure

As shown in the right image below, the participants were instructed to look at a moving red ball as a fixation target during the data collection. The position of the red ball in the visual field of the participant is shown in middle image below with an image captured by the scene camera. In order to cover as many different conditions as possible, we randomly picked the recording locations in and around of several buildings. Each location was not chosen more than once during the whole recording of all participants. 34.3% of the recordings were done outdoors, in 84.7% natural light was present and in 33.6% artificial light was present. Besides locations, we have also tweaked the angle of the eye cameras such that the dataset contains a wide range of camera angles from frontal views to highly off-axis angles. This is done by either asking the participant to take the tracker off and put it back on, or manually moving the camera. With each of the 22 participant we recorded three videos with around 20 seconds length, yielding 130,856 images overall.Participants could keep their glasses and contact lenses on during the recording.

Data Annotation

We used different methods for annotation. In many easy cases such as some indoor recordings, the pupil area has a clear boundary and no strong reflections inside. We annotated these frames by manually selecting 1 or 2 points inside the pupil area, using them as seed points to find the largest connected area with similar intensity values. The pupil center is defined as the centroid of this area. Some recordings have a clear scene video but strong reflections/noise in the eye video, such as outdoor recordings under strong sunlight. In those cases, we tracked the fixation target (red ball) in the scene videos and manually annotated part of the eye pupil positions in the eye videos. From this calibration data we com- puted a mapping function from target positions to pupil positions. In addition, we examined the annotated videos again to find wrong annotations, and corrected them by selecting 5 or more points on the pupil boundary and fitting an ellipse to them. The center of the ellipse was used as a refined pupil center position.

Citation

@inproceedings{tonsen2016labelled,
  title={Labelled pupils in the wild: a dataset for studying pupil detection in unconstrained
environments},
  author={Tonsen, Marc and Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas},
  booktitle={Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking
Research \& Applications},
  pages={139--142},
  year={2016}
}

License

Custom

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