Mapping Functions in Gaze Tracking

Sheela, Sheenu; Vijaya, P. A.

doi:10.5120/3080-4216

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Outstanding work has been done to understand the relationship between eye movements, semantic description, and computer vision [32], [33]. Gaze-tracking systems include examination of eyeball features (such as pupil, eyelid, and iris), gaze direction evaluation, a gaze mapping function, and hardware calibration as described in [34]. A benchmark for the point of gaze detection algorithms is presented by McMurrough et al in [35].…”

Section: Eye-tracking Data Analysis Methodologymentioning

confidence: 99%

Software Architecture for Automating Cognitive Science Eye-Tracking Data Analysis and Object Annotation

Panetta

Wan

Kaszowska

et al. 2019

IEEE Trans. Human-Mach. Syst.

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The advancement of wearable eye-tracking technology enables cognitive researchers to capture vast amounts of eye gaze information while participants are completing specific tasks without restrictions on their movement. However, while eye trackers can overlay a gaze indicator on the scene video, identifying the specific objects being looked at and analyzing the resulting dataset are accomplished mostly by manual annotation. This method is a cost-prohibitive and time-consuming approach that is prone to human error. Such analytic difficulty limits researchers' ability to data mine the information efficiently, ultimately restricting the number of scenarios that can feasibly be conducted within budget. Here, the first fully automated solution for eye-tracking data analysis is presented, which eliminates the need for manual annotation. The proposed software architecture, gaze to object classification (GoC), processes the gaze-overlaid video from commercially available wearable eye trackers, recognizes and classifies the specific object a user is focusing on and calculates the gaze duration time. GoC utilizes an image cross-correlation method to locate the gaze indicator and an image similarity measurement to support faster processing. The presented system has been successfully adopted by cognitive psychologists. GoC's exceptional performance in analyzing a case study spanning over 50 h of mobile eye-tracking is presented. The accuracy and a cost-analysis comparison between GoC and state-of-the-art manual annotation software are provided. GoC has game-changing potential for increasing the ecological validity of using eye-tracking technology in cognitive research.

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Section: Eye-tracking Data Analysis Methodologymentioning

confidence: 99%

Software Architecture for Automating Cognitive Science Eye-Tracking Data Analysis and Object Annotation

Panetta

Wan

Kaszowska

et al. 2019

IEEE Trans. Human-Mach. Syst.

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“…Appearance-based techniques use image content to estimate gaze direction by mapping image data to screen coordinates [59,60]. The major appearance-based methods [61] are based on morphable model [62], gray scale unit images [52], appearance manifold [63], Gaussian interpolation [64] and cross-ratio [65]. Appearance-based methods typically do not require calibration of cameras and geometry data since the mapping is made directly on the image contents.…”

Section: Appearance-based Gaze Estimationmentioning

confidence: 99%

Robust Discriminative Analysis Framework for Gaze and Headpose Estimation

Rabba¹

2021

Preprint

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Head movements, combined with gaze, play a fundamental role in predicting a person’s action and intention. In non-constrained head movement settings, the process is complex, and performance can degrade significantly in the presence of variation in head-pose, gaze position, occlusion and ambient illumination. In this thesis, a framework is therefore proposed to fuse and combine head-pose and gaze information to obtain more robust and accurate gaze estimation. Specific contributions include: the development of a newly developed graph-based model for pupil localization and accurate estimation of the pupil center; the proposal of a novel iris region descriptor feature using quadtree decomposition, that works together with pupil localization for gaze estimation; the proposal of kernel-based extensions and enhancements to a fusion mechanism known as Discriminative Multiple Canonical Correlation Analysis (DMCCA) for fusing features (proposed and traditional) together, to generate a refined, high quality feature set for classification; and the newly developed methodology of head-pose features based on quadtree decompositions and geometrical moments, to better integrate roll, yaw, pitch and jawline into the overall estimation framework. The experimental results of the proposed framework demonstrate robustness against variations in illumination, occlusion, head-pose and is calibration free. The proposed framework was validated on several datasets and scored: 4.5° using MPII, 4.4° using Cave, 4.8° using EYEDIAP, 5.0° using ACS, 4.1° using OSLO and 4.5° using UULM datasets respectively.

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“…Afterward, a plethora of new works have appeared in the literature, and then it has become more useful and fruitful to focus on every single aspect of the process rather than on the entire algorithmic pipeline. This led authors in [ 21 ] to review methods for eye region and pupil detection and localization, whereas, in [ 22 ], the gaze mapping functions based on interpolation or approximation that determine, starting from the pupil glint, the coordinates on a screen, are summarized. Works dealing with full-face appearance-based gaze estimation have been discussed in [ 23 ] and an overview of key technologies of gaze tracking has been proposed in [ 24 ] instead.…”

Section: Introductionmentioning

confidence: 99%

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

Cazzato

Leo

Distante

et al. 2020

Sensors

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The automatic detection of eye positions, their temporal consistency, and their mapping into a line of sight in the real world (to find where a person is looking at) is reported in the scientific literature as gaze tracking. This has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. A very long journey has been made from the first pioneering works, and this continuous search for more accurate solutions process has been further boosted in the last decade when deep neural networks have revolutionized the whole machine learning area, and gaze tracking as well. In this arena, it is being increasingly useful to find guidance through survey/review articles collecting most relevant works and putting clear pros and cons of existing techniques, also by introducing a precise taxonomy. This kind of manuscripts allows researchers and technicians to choose the better way to move towards their application or scientific goals. In the literature, there exist holistic and specifically technological survey documents (even if not updated), but, unfortunately, there is not an overview discussing how the great advancements in computer vision have impacted gaze tracking. Thus, this work represents an attempt to fill this gap, also introducing a wider point of view that brings to a new taxonomy (extending the consolidated ones) by considering gaze tracking as a more exhaustive task that aims at estimating gaze target from different perspectives: from the eye of the beholder (first-person view), from an external camera framing the beholder’s, from a third-person view looking at the scene where the beholder is placed in, and from an external view independent from the beholder.

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Mapping Functions in Gaze Tracking

Cited by 6 publications

References 23 publications

Software Architecture for Automating Cognitive Science Eye-Tracking Data Analysis and Object Annotation

Software Architecture for Automating Cognitive Science Eye-Tracking Data Analysis and Object Annotation

Robust Discriminative Analysis Framework for Gaze and Headpose Estimation

When I Look into Your Eyes: A Survey on Computer Vision Contributions for Human Gaze Estimation and Tracking

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