Showing papers on "Eye tracking published in 2023"

In the Eye of the Beholder: Gaze and Actions in First Person Video

Abstract: We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera. To facilitate our research, we first introduce the EGTEA Gaze+ dataset. Our dataset comes with videos, gaze tracking data, hand masks and action annotations, thereby providing the most comprehensive benchmark for First Person Vision (FPV). Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV. Our method describes the participant's gaze as a probabilistic variable and models its distribution using stochastic units in a deep network. We further sample from these stochastic units, generating an attention map to guide the aggregation of visual features for action recognition. Our method is evaluated on our EGTEA Gaze+ dataset and achieves a performance level that exceeds the state-of-the-art by a significant margin. More importantly, we demonstrate that our model can be applied to larger scale FPV dataset—EPIC-Kitchens even without using gaze, offering new state-of-the-art results on FPV action recognition.

Review of Eye Tracking Metrics Involved in Emotional and Cognitive Processes

Abstract: Eye behaviour provides valuable information revealing one's higher cognitive functions and state of affect. Although eye tracking is gaining ground in the research community, it is not yet a popular approach for the detection of emotional and cognitive states. In this paper, we present a review of eye and pupil tracking related metrics (such as gaze, fixations, saccades, blinks, pupil size variation, etc.) utilized towards the detection of emotional and cognitive processes, focusing on visual attention, emotional arousal and cognitive workload. Besides, we investigate their involvement as well as the computational recognition methods employed for the reliable emotional and cognitive assessment. The publicly available datasets employed in relevant research efforts were collected and their specifications and other pertinent details are described. The multimodal approaches which combine eye-tracking features with other modalities (e.g. biosignals), along with artificial intelligence and machine learning techniques were also surveyed in terms of their recognition/classification accuracy. The limitations, current open research problems and prospective future research directions were discussed for the usage of eye-tracking as the primary sensor modality. This study aims to comprehensively present the most robust and significant eye/pupil metrics based on available literature towards the development of a robust emotional or cognitive computational model.

A Reliable Sample Selection Strategy for Weakly Supervised Visual Tracking

Abstract: Reliability is an important property in the applied engineering systems, especially in visual tracking. The supervised visual tracking method uses reliable ground truth that is manually annotated, which is hard to get in many applications. However, weakly supervised visual trackings are limited by the low-quality labels. Therefore, a reliable sample selection strategy is the most important issue for the weakly supervised visual trackings. In this article, we propose an optimal sample selection strategy and apply it to the visual tracking system. The strategy first assesses the reliability of the samples according to the score map, where the score map is the pseudolabel generated by the upstream task to meet the needs of the downstream task. Then, the unreliable pseudolabels are replaced by reliable ground truth or discarded to overcome the degraded modeling problem by filtering low-quality samples. Finally, through comparison with multiple selection strategies, it is verified that the model trained using this strategy has the best performance. The proposed visual tracking model achieves the best performance among multiple assessment metrics in multiple datasets. Experiments verify that the scientific sample quality assessment method is very important. It can guide the improvement of model performance, which is of great help to the weakly supervised learning systems based on data.

Eye Tracking in Virtual Reality: a Broad Review of Applications and Challenges

Abstract: Abstract Eye tracking is becoming increasingly available in head-mounted virtual reality displays with various headsets with integrated eye trackers already commercially available. The applications of eye tracking in virtual reality are highly diversified and span multiple disciplines. As a result, the number of peer-reviewed publications that study eye tracking applications has surged in recent years. We performed a broad review to comprehensively search academic literature databases with the aim of assessing the extent of published research dealing with applications of eye tracking in virtual reality, and highlighting challenges, limitations and areas for future research.

Predicting Gaze-based Target Selection in Augmented Reality Headsets based on Eye and Head Endpoint Distributions

Abstract: Target selection is a fundamental task in interactive Augmented Reality (AR) systems. Predicting the intended target of selection in such systems can provide users with a smooth, low-friction interaction experience. Our work aims to predict gaze-based target selection in AR headsets with eye and head endpoint distributions, which describe the probability distribution of eye and head 3D orientation when a user triggers a selection input. We first conducted a user study to collect users’ eye and head behavior in a gaze-based pointing selection task with two confirmation mechanisms (air tap and blinking). Based on the study results, we then built two models: a unimodal model using only eye endpoints and a multimodal model using both eye and head endpoints. Results from a second user study showed that the pointing accuracy is improved by approximately 32% after integrating our models into gaze-based selection techniques.

Teachers' professional vision in action

Abstract: Abstract. In need of simultaneously tackling various tasks at a fast pace, teaching is a job that requires skillful attention allocation. Selective visual attention forms the basis of teacher's professional vision – the expertise of attending to and interpreting classroom features, but it is also a process mostly hidden from direct observation. Eye tracking can capture this otherwise invisible attentional process and has long been used in demonstrating the visual expertise in various skill domains. Yet, the relationship between expertise and teachers' eye movements during real-life teaching remains a seldom explored area. The current study investigated the distinctive features of teachers' gaze in relation to their expertise levels. Specifically, eye movements were collected from 25 pairs of expert and novice teachers, with each pair teaching in the same classroom and with the same content. The eye movements were analyzed using scanpath comparison and point pattern analysis method. Results revealed that compared with novices, expert teachers had overall shorter fixation durations and larger quantity of fixations. They also had smaller proportion of fixations directed to objects irrelevant to teaching and the distribution of their fixations were wider. These results demonstrated that teachers had distinctive eye movement features in relation to their expertise levels. Most importantly, expert teachers exhibited better selective attention – a key component of professional vision. The implications regarding teacher education and instruction were also discussed.

Semi-supervised Visual Tracking of Marine Animals Using Autonomous Underwater Vehicles

Abstract: In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild.

Brain-Controlled, AR-Based Home Automation System Using SSVEP-Based Brain-Computer Interface and EOG-Based Eye Tracker: A Feasibility Study for the Elderly End User

Abstract: Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user’s preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.

Visual Attention and Recognition Differences Based on Expertise in a Map Reading and Memorability Study

Abstract: This study investigates how expert and novice map users’ attention is influenced by the map design characteristics of 2D web maps by building and sharing a framework to analyze large volumes of eye tracking data. Our goal is to respond to the following research questions: (i) which map landmarks are easily remembered? (memorability), (ii) how are task difficulty and recognition performance associated? (task difficulty), and (iii) how do experts and novices differ in terms of recognition performance? (expertise). In this context, we developed an automated area-of-interest (AOI) analysis framework to evaluate participants’ fixation durations, and to assess the influence of linear and polygonal map features on spatial memory. Our results demonstrate task-relevant attention patterns by all participants, and better selective attention allocation by experts. However, overall, we observe that task type and map feature type mattered more than expertise when remembering the map content. Predominantly polygonal map features such as hydrographic areas and road junctions serve as attentive features in terms of map reading and memorability. We make our dataset entitled CartoGAZE publicly available.

EHTask: Recognizing User Tasks From Eye and Head Movements in Immersive Virtual Reality

Abstract: Understanding human visual attention in immersive virtual reality (VR) is crucial for many important applications, including gaze prediction, gaze guidance, and gaze-contingent rendering. However, previous works on visual attention analysis typically only explored one specific VR task and paid less attention to the differences between different tasks. Moreover, existing task recognition methods typically focused on 2D viewing conditions and only explored the effectiveness of human eye movements. We first collect eye and head movements of 30 participants performing four tasks, i.e., Free viewing , Visual search , Saliency , and Track , in 15 360-degree VR videos. Using this dataset, we analyze the patterns of human eye and head movements and reveal significant differences across different tasks in terms of fixation duration, saccade amplitude, head rotation velocity, and eye-head coordination. We then propose EHTask – a novel learning-based method that employs eye and head movements to recognize user tasks in VR. We show that our method significantly outperforms the state-of-the-art methods derived from 2D viewing conditions both on our dataset (accuracy of $84.4\%$ versus $62.8\%$ ) and on a real-world dataset ( $61.9\%$ versus $44.1\%$ ). As such, our work provides meaningful insights into human visual attention under different VR tasks and guides future work on recognizing user tasks in VR.

Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models

Abstract: A single digital newsletter usually contains many messages (regions). Users’ reading time spent on, and read level (skip/skim/read-in-detail) of each message is important for platforms to understand their users’ interests, personalize their contents, and make recommendations. Based on accurate but expensive-to-collect eyetracker-recorded data, we built models that predict per-region reading time based on easy-to-collect Javascript browser tracking data. With eye-tracking, we collected 200k ground-truth datapoints on participants reading news on browsers. Then we trained machine learning and deep learning models to predict message-level reading time based on user interactions like mouse position, scrolling, and clicking. We reached 27% percentage error in reading time estimation with a two-tower neural network based on user interactions only, against the eye-tracking ground truth data, while the heuristic baselines have around 46% percentage error. We also discovered the benefits of replacing per-session models with per-timestamp models, and adding user pattern features. We concluded with suggestions on developing message-level reading estimation techniques based on available data.

LSOTB-TIR: A Large-Scale High-Diversity Thermal Infrared Single Object Tracking Benchmark.

Abstract: Unlike visual object tracking, thermal infrared (TIR) object tracking methods can track the target of interest in poor visibility such as rain, snow, and fog, or even in total darkness. This feature brings a wide range of application prospects for TIR object-tracking methods. However, this field lacks a unified and large-scale training and evaluation benchmark, which has severely hindered its development. To this end, we present a large-scale and high-diversity unified TIR single object tracking benchmark, called LSOTB-TIR, which consists of a tracking evaluation dataset and a general training dataset with a total of 1416 TIR sequences and more than 643 K frames. We annotate the bounding box of objects in every frame of all sequences and generate over 770 K bounding boxes in total. To the best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object tracking benchmark to date. We spilt the evaluation dataset into a short-term tracking subset and a long-term tracking subset to evaluate trackers using different paradigms. What's more, to evaluate a tracker on different attributes, we also define four scenario attributes and 12 challenge attributes in the short-term tracking evaluation subset. By releasing LSOTB-TIR, we encourage the community to develop deep learning-based TIR trackers and evaluate them fairly and comprehensively. We evaluate and analyze 40 trackers on LSOTB-TIR to provide a series of baselines and give some insights and future research directions in TIR object tracking. Furthermore, we retrain several representative deep trackers on LSOTB-TIR, and their results demonstrate that the proposed training dataset significantly improves the performance of deep TIR trackers. Codes and dataset are available at https://github.com/QiaoLiuHit/LSOTB-TIR.

Effect of Timing Sequence of Brand Promotion in an Online Video-an Eye-Tracking Study

Abstract: Embedded marketing in online videos has become a popular form of advertising. This study aims to examine the impact of visual attention due to different sequences of product placement by content creators in their videos on brand name, product name, and brand awareness. An experimental study was conducted to investigate the effect on the viewer's attention while viewing a video based on the placement of the brand at the beginning, in the middle, and towards the end of the video. The study used an eye-tracking experiment, where fixation count and fixation duration represented visual attention and engagement, which were triangulated with a questionnaire survey. The study found a partially significant difference in visual attention due to different sequences of product promotion by the content creator, but an insignificant impact on brand awareness. The findings suggest that product placement at the beginning of the video drew more attention to the brand name and product name than the other two sequences. In particular, the frequency of attention was highest on product placement at the beginning of the video, followed by at the end of the video and in the middle of the video, respectively. Additionally, the heat map showed that the product name and brand name received the most attention among other elements. However, the three different sequences of product placement did not impact the brand awareness of the consumer. The results of this study provide new insights for content creators and advertisers when creating videos with embedded brand promotions. This study explores the impact of different sequences of product placement in a video using eye-tracking technology, allowing us to understand the subconscious behavior of the viewer and how it may impact their buying decision. This experiment will help embedded marketers understand the impact of the sequence of promoting a product/brand in a video on engagement levels, providing a glimpse into the respondent's subconscious mind.

Improving Intention Detection in Single-Trial Classification Through Fusion of EEG and Eye-Tracker Data

Abstract: Intention decoding is an indispensable procedure in hands-free human–computer interaction (HCI). A conventional eye-tracker system using a single-model fixation duration may issue commands that ignore users' real expectations. Here, an eye-brain hybrid brain–computer interface (BCI) interaction system was introduced for intention detection through the fusion of multimodal eye-tracker and event-related potential (ERP) [a measurement derived from electroencephalography (EEG)] features. Eye-tracking and EEG data were recorded from 64 healthy participants as they performed a 40-min customized free search task of a fixed target icon among 25 icons. The corresponding fixation duration of eye tracking and ERP were extracted. Five previously-validated linear discriminant analysis (LDA)-based classifiers [including regularized LDA, stepwise LDA, Bayesian LDA, shrinkage linear discriminant analysis (SKLDA), and spatial-temporal discriminant analysis] and the widely-used convolutional neural network (CNN) method were adopted to verify the efficacy of feature fusion from both offline and pseudo-online analysis, and the optimal approach was evaluated by modulating the training set and system response duration. Our study demonstrated that the input of multimodal eye tracking and ERP features achieved a superior performance of intention detection in the single-trial classification of active search tasks. Compared with the single-model ERP feature, this new strategy also induced congruent accuracy across classifiers. Moreover, in comparison with other classification methods, we found that SKLDA exhibited a superior performance when fusing features in offline tests (ACC = 0.8783, AUC = 0.9004) and online simulations with various sample amounts and duration lengths. In summary, this study revealed a novel and effective approach for intention classification using an eye-brain hybrid BCI and further supported the real-life application of hands-free HCI in a more precise and stable manner.

Eye Tracking in Virtual Reality.

Abstract: This chapter explores the current state of the art in eye tracking within 3D virtual environments. It begins with the motivation for eye tracking in Virtual Reality (VR) in psychological research, followed by descriptions of the hardware and software used for presenting virtual environments as well as for tracking eye and head movements in VR. This is followed by a detailed description of an example project on eye and head tracking while observers look at 360° panoramic scenes. The example is illustrated with descriptions of the user interface and program excerpts to show the measurement of eye and head movements in VR. The chapter continues with fundamentals of data analysis, in particular methods for the determination of fixations and saccades when viewing spherical displays. We then extend these methodological considerations to determining the spatial and temporal coordination of the eyes and head in VR perception. The chapter concludes with a discussion of outstanding problems and future directions for conducting eye- and head-tracking research in VR. We hope that this chapter will serve as a primer for those intending to implement VR eye tracking in their own research.

Telestration with augmented reality improves surgical performance through gaze guidance

Abstract: Abstract Background In minimally invasive surgery (MIS), trainees need to learn how to interpret the operative field displayed on the laparoscopic screen. Experts currently guide trainees mainly verbally during laparoscopic procedures. A newly developed telestration system with augmented reality (iSurgeon) allows the instructor to display hand gestures in real-time on the laparoscopic screen in augmented reality to provide visual expert guidance (telestration). This study analysed the effect of telestration guided instructions on gaze behaviour during MIS training. Methods In a randomized-controlled crossover study, 40 MIS naive medical students performed 8 laparoscopic tasks with telestration or with verbal instructions only. Pupil Core eye-tracking glasses were used to capture the instructor’s and trainees’ gazes. Gaze behaviour measures for tasks 1–7 were gaze latency, gaze convergence and collaborative gaze convergence. Performance measures included the number of errors in tasks 1–7 and trainee’s ratings in structured and standardized performance scores in task 8 (ex vivo porcine laparoscopic cholecystectomy). Results There was a significant improvement 1–7 on gaze latency [ F (1,39) = 762.5, p < 0.01, η p 2 = 0.95], gaze convergence [ F (1,39) = 482.8, p < 0.01, η p 2 = 0.93] and collaborative gaze convergence [ F (1,39) = 408.4, p < 0.01, η p 2 = 0.91] upon instruction with iSurgeon. The number of errors was significantly lower in tasks 1–7 (0.18 ± 0.56 vs. 1.94 ± 1.80, p < 0.01) and the score ratings for laparoscopic cholecystectomy were significantly higher with telestration (global OSATS: 29 ± 2.5 vs. 25 ± 5.5, p < 0.01; task-specific OSATS: 60 ± 3 vs. 50 ± 6, p < 0.01). Conclusions Telestration with augmented reality successfully improved surgical performance. The trainee’s gaze behaviour was improved by reducing the time from instruction to fixation on targets and leading to a higher convergence of the instructor’s and the trainee’s gazes. Also, the convergence of trainee’s gaze and target areas increased with telestration. This confirms augmented reality-based telestration works by means of gaze guidance in MIS and could be used to improve training outcomes.

The Role of Eco-Labels in Making Environmentally Friendly Choices: An Eye-Tracking Study on Aquaculture Products with Italian Consumers

Abstract: Eco-labels are crucial in helping consumers make sustainable food choices. However, previous literature has shown that eco-labels lack visibility and, frequently, are not easy for consumers to see. The main goal of the present study was to analyse the influence of aquaculture eco-labels’ visual elements—size and saliency—on consumers’ visual attention and choice. The study uses an eye-tracking methodology, together with a choice experiment and a semiotic analysis. A word association (WA) task was used to explore how each eco-label’s graphic design influenced consumers’ perceptions. Sixty-one consumers’ eye movements were tracked while choosing smoked salmon and seabass products carrying different eco-labels. The results showed that size and saliency largely influence visual attention. The choice of aquaculture products was influenced only by the size of the eco-labels. According to the WA task, the shape, the symbols and the language in which the claim was written influenced consumers’ preferences. The findings contribute to marketing and food research, suggesting which visual elements should be considered to increase consumers’ interest in eco-labels.

Gazing through the bubble: an experimental investigation into financial risk-taking using eye-tracking

Abstract: Abstract Eye tracking can facilitate understanding irrational decision-making in contexts such as financial risk-taking. For this purpose, we develop an experimental framework in which participants trade a risky asset in a simulated bubble market to maximize individual returns while their eye movements are recorded. Returns are sensitive to eye movement dynamics, depending on the presented visual stimuli. Using eye-tracking data, we investigated the effects of arousal, attention, and disengagement on individual payoffs using linear and nonlinear approaches. By estimating a nonlinear model using attention as a threshold variable, our results suggest that arousal positively influences trading returns, but its effect becomes smaller when attention exceeds a certain threshold, whereas disengagement has a higher negative impact on reduced attention levels and becomes almost irrelevant when attention increases. Hence, we provide a neurobehavioral metric as a function of attention that predicts financial gains in boom-and-bust scenarios. This study serves as a proof-of-concept for developing future psychometric measures to enhance decision-making.

Potential of eye-tracking for interactive geovisual exploration aided by machine learning

Abstract: This review article collects knowledge on the use of eye-tracking and machine learning methods for application in automated and interactive geovisualization systems. Our focus is on exploratory reading of geovisualizations (abbr. geoexploration) and on machine learning tools for exploring vector geospatial data. We particularly consider geospatial data that is unlabeled, confusing or unknown to the user. The contribution of the article is in (i) defining principles and requirements for enabling user interaction with the geovisualizations that learn from and adapt to user behavior, and (ii) reviewing the use of eye tracking and machine learning to design gaze-aware interactive map systems (GAIMS). In this context, we review literature on (i) human-computer interaction (HCI) design for exploring geospatial data, (ii) eye tracking for cartographic user experience, and (iii) machine learning applied to vector geospatial data. The review indicates that combining eye tracking and machine learning is promising in terms of assisting geoexploration. However, more research is needed on eye tracking for interaction and personalization of cartographic/map interfaces as well as on machine learning for detection of geometries in vector format.

Neurotypical, but not autistic, adults might experience distress when looking at someone avoiding eye contact: A live face-to-face paradigm.

Abstract: LAY ABSTRACT What is already known about the topic?Autistics are usually reported to share less eye contact than neurotypicals with their interlocutors. However, the reason why autistics might pay less attention to eyes looking at them is still unknown: some autistics express being hyper-aroused by this eye contact, while some eye-tracking studies suggest that eye contact is associated with hypo-arousal in autism.What this paper adds?This study is based on a highly controlled live face-to-face paradigm, combining a wearable eye-tracker (to study eye behaviours) with electrodermal activity sensors (to assess potential stress). We draw a nuanced picture of social attention in autism, as our autistic participants did not differ from our neurotypical group in their eye behaviours nor their skin conductance responses. However, we found that neurotypicals, compared to autistics, seemed to be much more distressed when their interlocutor did not gaze at them during the experiment.Implications for practice, research or policy:Our study encourages to consider social interaction difficulties in autism as a relational issue, instead as an individual deficit. This step might be first taken in research, by implementing paradigms sensitive to the experimenter's role and attitude.

Is visual gaze in children with autism spectrum disorder related to sequence of emotion intensity presentation? An eye‐tracking study of natural emotion perception processes

Abstract: Emotion cognitive remediation is a critical component of social skills training for children with autism spectrum disorder (ASD). Visual perception of emotions is highly correlated with the intensity and sequence of presented emotions. However, few studies examined the effect of presentation sequence and intensity on emotion perception. The present study examined the gaze patterns of children with ASD in receiving different sequences of emotion presentation using eye‐tracking technologies. Gaze patterns of ecologically‐valid video clips of silent emotion stimuli by 51 ASD children and 34 typically developing (TD) children were recorded. Results indicated that ASD and TD children showed opposite visual fixation during different intensity presentation modes: children with ASD showed better emotion perception with a weak‐to‐strong emotion sequence when presented. The visual reductions in emotion perception in children with ASD may due to different perceptual threshold to emotional intensity. The extent of the reductions could be related to an individual's Personal‐Social ability. The present study supports the importance of intensity of emotions and the order at which the emotional stimuli were presented in yielding better emotion perceptions in children with ASD, suggesting that the order of emotion presentation may potentially influence emotion processing during ASD rehabilitation. It is anticipated that the present findings could bring more insights to clinicians for intervention planning in the future.

Effects of Anthropomorphic Design Cues of Chatbots on Users’ Perception and Visual Behaviors

Abstract: Measurement of users’ perception and visual behaviors to anthropomorphic design cues of chatbots can improve our understanding of chatbots and potentially optimize chatbot design. However, as two typical and basic features, how chatbot appearances and conversational styles jointly affect users’ perception and visual behaviors remains unclear. Therefore, this study conducted an eye-tracking experiment to explore users’ perception and visual behaviors. Results indicate that anthropomorphic appearances and human-like conversational styles jointly increased users’ perception of chatbots’ social presence, trust in chatbots, and satisfaction with chatbots. In contrast, on users’ visual behaviors, such a joint effect was not found, although chatbots with higher anthropomorphic appearances and human-like conversational styles triggered more fixation counts and longer dwell time. These findings suggest that anthropomorphic appearance and human-like conversational style can improve users’ perception and attract more visual attention to chatbots. These findings provide theoretical contributions and practical implications for relevant researchers and designers.

Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

Abstract: The distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real‐world environments. Our method is based on a space‐time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think‐aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.

An Automatic Calibration Method for Kappa Angle Based on a Binocular Gaze Constraint

Abstract: Kappa-angle calibration shows its importance in gaze tracking due to the special structure of the eyeball. In a 3D gaze-tracking system, after the optical axis of the eyeball is reconstructed, the kappa angle is needed to convert the optical axis of the eyeball to the real gaze direction. At present, most of the kappa-angle-calibration methods use explicit user calibration. Before eye-gaze tracking, the user needs to look at some pre-defined calibration points on the screen, thereby providing some corresponding optical and visual axes of the eyeball with which to calculate the kappa angle. Especially when multi-point user calibration is required, the calibration process is relatively complicated. In this paper, a method that can automatically calibrate the kappa angle during screen browsing is proposed. Based on the 3D corneal centers and optical axes of both eyes, the optimal objective function of the kappa angle is established according to the coplanar constraint of the visual axes of the left and right eyes, and the differential evolution algorithm is used to iterate through kappa angles according to the theoretical angular constraint of the kappa angle. The experiments show that the proposed method can make the gaze accuracy reach 1.3° in the horizontal plane and 1.34° in the vertical plane, both of which are within the acceptable margins of gaze-estimation error. The demonstration of explicit kappa-angle calibration is of great significance to the realization of the instant use of gaze-tracking systems.