Algorithms for the estimation of gaze direction from mobile and video-based eye trackers typically involve tracking a feature of the eye that moves through the eye camera image in a way that covaries with the shifting gaze direction, such as the center or boundaries of the pupil. Tracking these features using traditional computer vision techniques can be difficult due to partial occlusion and environmental reflections. Although recent efforts to use machine learning (ML) for pupil tracking have demonstrated superior results when evaluated using standard measures of segmentation performance, little is known of how these networks may affect the quality of the final gaze estimate. This work provides an objective assessment of the impact of several contemporary ML-based methods for eye feature tracking when the subsequent gaze estimate is produced using either feature-based or model-based methods. Metrics include the accuracy and precision of the gaze estimate, as well as drop-out rate.
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Enhancing eye tracking for nonhuman primates and other subjects unable to follow instructions: Adaptive calibration and validation of Tobii eye trackers with the Titta toolbox
Accurate eye tracking is crucial for gaze-dependent research, but calibrating eye trackers in subjects who cannot follow instructions, such as human infants and nonhuman primates, presents a challenge. Traditional calibration methods rely on verbal instructions, which are ineffective for these populations. To address this, researchers often use attention-grabbing stimuli in known locations; however, existing software for video-based calibration is often proprietary and inflexible. We introduce an extension to the open-source toolbox Titta—a software package integrating desktop Tobii eye trackers with PsychToolbox experiments—to facilitate custom video-based calibration. This toolbox extension offers a flexible platform for attracting attention, calibrating using flexible point selection, and validating the calibration. The toolbox has been refined through extensive use with chimpanzees, baboons, and macaques, demonstrating its effectiveness across species. Our adaptive calibration and validation procedures provide a standardized method for achieving more accurate gaze tracking, enhancing gaze accuracy across diverse species.
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- Award ID(s):
- 1926327
- PAR ID:
- 10651250
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Behavior Research Methods
- Volume:
- 57
- Issue:
- 1
- ISSN:
- 1554-3528
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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