Advanced Gaze Visualizations for Three-dimensional Virtual Environments
                                         Sophie St...
2.2 Models of Interest Timeline

                                                                               A timeline...
Figure 3: An example of the models of interest timeline with selection markers for confining displayed scan and camera path...
Figure 6: Agreement ratings from both groups with the corresponding standard deviations.


pant of group 2, a short introd...
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Stellmach Advanced Gaze Visualizations For Three Dimensional Virtual Environments

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Gaze visualizations represent an effective way for gaining fast insights into eye tracking data. Current approaches do not adequately support eye tracking studies for three-dimensional (3D) virtual environments. Hence, we propose a set of advanced gaze visualization techniques for supporting gaze behavior analysis in such environments. Similar to commonly used gaze visualizations for twodimensional
stimuli (e.g., images and websites), we contribute advanced 3D scan paths and 3D attentional maps. In addition, we introduce a models of interest timeline depicting viewed models, which can be used for displaying scan paths in a selected time segment. A prototype toolkit is also discussed which combines an implementation of our proposed techniques. Their potential for facilitating eye tracking studies in virtual environments was supported by a user study among eye tracking and visualization experts.

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Stellmach Advanced Gaze Visualizations For Three Dimensional Virtual Environments

  1. 1. Advanced Gaze Visualizations for Three-dimensional Virtual Environments Sophie Stellmach∗ , Lennart Nacke∗∗ , Raimund Dachselt∗ ∗ User Interface & Software Engineering Group ∗∗ Game Systems and Interaction Research Laboratory Faculty of Computer Science School of Computing Otto-von-Guericke-Universit¨ t Magdeburg a Blekinge Institute of Technology D-39106 Magdeburg, Germany SE-37225 Ronneby, Sweden stellmach@acm.org, dachselt@acm.org lennart.nacke@acm.org Abstract adapted to challenges implied by 3D space (e.g., occlusion prob- lems or varying viewpoints). On the other hand, they do not allow Gaze visualizations represent an effective way for gaining fast in- for quickly determining which parts of a 3D model were viewed the sights into eye tracking data. Current approaches do not adequately longest. After all, this lack of suitable techniques currently results support eye tracking studies for three-dimensional (3D) virtual en- in a time-consuming evaluation of eye tracking studies in 3D VEs. vironments. Hence, we propose a set of advanced gaze visualiza- Usually, video recordings of displayed content have to be examined tion techniques for supporting gaze behavior analysis in such envi- frame-by-frame, for which gaze data are represented as superim- ronments. Similar to commonly used gaze visualizations for two- posed gaze plots. Thus, more suitable techniques are required for a dimensional stimuli (e.g., images and websites), we contribute ad- faster visual gaze analysis in various application domains (e.g., vir- vanced 3D scan paths and 3D attentional maps. In addition, we tual interactive trainings [Duchowski et al. 2000], social network- introduce a models of interest timeline depicting viewed models, ing environments, and computer-aided design). Such techniques which can be used for displaying scan paths in a selected time seg- could, for example, assist in quickly examining 3D product design ment. A prototype toolkit is also discussed which combines an im- features or people’s attentional foci when being confronted with a plementation of our proposed techniques. Their potential for facil- critical situation. itating eye tracking studies in virtual environments was supported by a user study among eye tracking and visualization experts. This paper makes a methodological and technological contribution to the investigation of visual attention in 3D VEs. To facilitate eye tracking studies in 3D VEs, we propose a set of adapted and novel CR Categories: H.5.1 [Multimedia Information Systems]: gaze visualization techniques: superimposed 3D scan paths and 3D Evaluation/Methodology—Information Interfaces and Presentation attentional maps, as well as a models of interest timeline depicting I.4.8 [Computing Methodologies]: Image Processing and Com- viewed models. We have also developed a gaze analysis tool for puter Vision—Scene Analysis the evaluation of eye tracking studies in static 3D VEs. The tech- niques and the tool are described together with related work in the Keywords: Gaze visualizations, eye tracking, eye movements, at- following section. In Section 3, a survey is discussed which was tentional maps, scan paths, virtual environments, three-dimensional conducted among eye tracking and visualization experts for assess- ing the usefulness of our proposed techniques. 1 Introduction 2 Gaze Visualization Techniques for Three- For investigating gaze behavior, large sets of numerical data have to be collected and evaluated, such as eye positions, calculated fix- dimensional Virtual Environments ations and saccades, as well as total fixation times [Ramloll et al. 2004]. Consequently, gaze visualizations have helped detecting, Common gaze visualization techniques for evaluating eye tracking understanding, and interpreting essential relationships in such mul- studies include scan paths and attentional maps but fall short when tivariate and multidimensional data. Especially superimposed tech- it comes to 3D VEs. For example, the gaze behavior of customers niques proved useful for imparting how a viewer’s gaze behaved in a virtual 3D store could be evaluated with regard to product and with respect to an underlying stimulus. Common two-dimensional advertisement placements. Potential research questions could ask (2D) gaze visualization techniques include scan paths, attentional in which order items were observed or how visual interest is gen- maps, and areas of interest. erally distributed. For answering questions like these, we introduce a set of gaze visualization techniques adapted for static 3D VEs. While traditional diagnostic eye tracking studies have focused on a These techniques include superimposed visualizations of 3D scan person’s gaze behavior while reading texts or observing images, paths and 3D attentional maps, as well as a models of interest time- new application areas have emerged in connection with the ad- line depicting the order of fixated objects. For them, a 3D scene is vancement of technology. This includes the perception of digi- assumed with several static models, which users can freely explore tal content such as websites, user interfaces, digital games, and and for which sample gaze data was collected. Furthermore, we three-dimensional (3D) virtual environments (VE). However, cur- presume a lack of transparent phenomena (e.g., fog or glass). Our rent gaze visualization techniques do not adequately support inter- approach uses 3D gaze positions which are calculated as intersec- active visual analysis of 3D VEs. On the one hand, they are not well tions between gaze rays and viewed models (see Section 2.4). Copyright © 2010 by the Association for Computing Machinery, Inc. 2.1 Three-dimensional Scan Paths Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the A common approach for depicting eye tracking data are scan path first page. Copyrights for components of this work owned by others than ACM must be visualizations superimposed on a stimulus representation. Scan honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on paths show the order of eye movements by drawing connected lines servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions Dept, ACM Inc., fax +1 (212) 869-0481 or e-mail (saccades) between subsequent gaze positions (fixations). Fixa- permissions@acm.org. tion and saccade plots imply data clustering by grouping infor- ETRA 2010, Austin, TX, March 22 – 24, 2010. © 2010 ACM 978-1-60558-994-7/10/0003 $10.00 109
  2. 2. 2.2 Models of Interest Timeline A timeline visualization maps data against time and facilitates find- ing events before, after, or during a given time interval. Thus, it can be used to answer several questions, such as: Has object x been observed repetitively? In what order and for how long are objects looked at? In this regard, the Object vs. Time View [Lessing and Linge 2002] is a series of timelines for depicting Areas of Interest (AOIs). Each AOI is assigned a time row, resulting in an AOI ma- Figure 1: Two alternative fixation representations, spheres (a) and trix. With an increasing number of AOIs and shorter fixation times, cones (b), are presented for 3D scan paths. the table size increases and may hinder a good overview. Therefore, we propose a space-filling models of interest (MOI) timeline (see Figure 3) for a compact illustration that effectively utilizes assigned screen space by omitting voids. We suggest to use the term Model of Interest to describe distinct 3D objects. The MOI timeline gives an overview about a user’s gaze distribution based on viewed models. Each model is labeled with a specific color, which can be manually adapted. By assigning the same value to different objects, semantic groups can be defined, for example, for group- ing similar looking models, object hierarchies or closely arranged items. A legend is displayed for providing an overview of assigned colors (see in the left area of Figure 5). Figure 2: Two examples of camera paths (viewpoints and viewing directions) and fixations. Zooming and selection techniques may aid in making data visible which would otherwise be suppressed due to limited display sizes. Based on TimeZoom [Dachselt and Weiland 2006], the MOI time- mation into meaningful units (fixations). While fixations indicate line can be dragged to a desired position to offer horizontal scrolling aspects attracting an observer’s attention, saccades provide infor- and supports continuous zooming (e.g., via a zoom slider, see Fig- mation about how fixations are related to each other. Thereby, fix- ure 3). For this purpose, the viewing area is divided into a large ations are usually displayed as circles varying in size depending on detail area for zooming (see Figure 3) and a small overview area the fixation duration and saccades as straight lines. displaying the entire collection. Displayed scan and camera paths can be filtered with respect to a selected period. For this purpose, Scan paths are frequently used for static (e.g., images and texts) navigation markers will appear if the path visualizations are em- and dynamic (e.g., videos) 2D stimuli. For other stimuli, superim- ployed (see Figure 3), otherwise the markers are hidden. The user posed fixation plots are usually applied to recorded videos of the can simply click and drag the markers to define intervals of inter- presented content. This often implies a time-consuming frame-by- est within the timeline. While dragging the timeline, the user can frame video data analysis. One solution proposed by Ramloll et al. play back data of interest as defined by this fixed interval. Finally, [2004] is a fixation net for dynamic 3D non-stereoscopic content, additional details for a timeline entry (e.g., object identifiers, start for which gaze positions are mapped onto flattened objects. Rep- and finish times) can be displayed, when hovering over it with the resenting binocular scan paths in 3D VRs has been presented by mouse cursor. Context menus are triggered by right-clicking on Duchowski et al. [2000] for which 2D gaze positions and depths items, for instance, for changing its color. (gaze depth paths) are depicted. In contrast to Duchowski et al., we propose a monocular scan path 2.3 Three-dimensional Attentional Maps depicting 3D gaze positions as intersections of a gaze ray and a viewed model. Besides traditional spherical representations (see An attentional map (or heatmap) is an aggregated representation Figure 1a), we used conical fixation representations pointing at a depicting areas of visual attention for primarily static 2D stimuli. corresponding gaze position (see Figure 1b), since it may integrate This is substantiated by the fact that an attentional map usually has additional information about varying camera positions. So, cones the same dimensions (width and height) as the underlying stimu- could represent gaze positions (apex), fixation durations (cone’s lus [Wooding 2002]. Since fixations are merely accumulated, at- base), viewing directions (cone’s orientation), and viewing dis- tentional maps do not provide information about gaze sequences, tances (cone’s height) within one representation. The traditional but are suitable for indicating areas attracting visual attention over saccade representation can cause problems in 3D, because saccade a certain period of time. Visualizing gaze data directly in the 3D lines may cross through surfaces. A simple solution we propose is VE enables an aggregated representation of longer observations (in to maintain the traditional saccade representation with the possibil- contrast to the traditional frame-by-frame evaluation). ity for adapting rendering options of 3D models (e.g., wireframe models or hiding objects) for determining linked fixations. We propose 3D attentional maps as superimposed aggregated vi- sualizations for virtual 3D stimuli: projected, object-based, and It is also important to visualize how the locations and viewing direc- surface-based attentional representations (see Figure 4). A pro- tions of the virtual camera have changed during observation. This jected attentional map is a 2D representation of 3D gaze distribu- may aid in finding out, for example, if a scene was observed from tions for selected arbitrary views (e.g. a top view as in Figure 4). diverse locations. We propose to visualize the camera path with For an object-based attentional representation one color is mapped traces pointing at the respective gaze positions. Figure 2 shows to the surface of each model based on its received visual attention an example for a camera path for which the camera locations are (see Figure 4b). This allows for quickly evaluating an object’s vi- depicted as red lines and viewing directions as straight blue lines. sual attractiveness while maintaining information about the spatial Displayed scan and camera paths can be filtered by means of the relationship with other models (e.g., two adjacent models have re- models of interest timeline to provide a better overview. ceived high visual attention). Surface-based attentional maps dis- 110
  3. 3. Figure 3: An example of the models of interest timeline with selection markers for confining displayed scan and camera paths. (a) Projected (top view) (b) Object-based (c) Surface-based Figure 4: Advanced attentional maps for the application in three- dimensional virtual environments. play aggregated fixation data as heatmaps directly on a model’s sur- face using a vertex-based mapping (see Figure 4c). This provides detailed insights into gaze distributions across models’ surfaces, al- lowing to draw quick conclusions about which model aspects at- tracted visual interest. Figure 5: A screenshot from SVEETER illustrating multiple views at a scene. The MOI timeline is shown in the lower area with its A combination of these techniques offers different levels of detail legend displayed to the upper left. for data investigation. While a projected heatmap may give an overview of the gaze distribution across a scene, an object-based attentional map can provide information about the models’ visual 3 User Study attractiveness when zooming in. Finally, the surface-based tech- nique allows for close examinations of viewed models. We have conducted a user study with eye tracking and visualization experts to assess the usefulness of the presented techniques. The method and results are discussed in the following paragraphs. 2.4 Implementation Participants. Group 1 consisted of 20 eye tracking professionals and researchers, aged between 23 and 52 years (Mean [M] = 34.50). For the implementation of the presented gaze visualization tech- Participants in this group rated their general eye tracking knowl- niques, a virtual 3D scene was required for which we used Mi- edge1 higher than average (M = 3.85, Standard Deviation [SD] = crosoft’s XNA Game Studio 3.0 (based on C#). Our system was 0.96). Group 2 included 8 local data visualization and computer confined to static 3D VEs without any transparent phenomena (e.g., graphics experts, aged between 25 and 35 years (M = 28.25). This smoke or semi-transparent textures). Users can freely explore the group rated their eye tracking knowledge under average (M = 1.25, scene by moving their camera viewpoints via mouse and keyboard SD = 1.09), but their expertise in computational visualization above controls. In addition, an integration of the Tobii 1750 Eye Tracker average (M = 3.63, SD = 1.22). and XNA allowed for logging 3D gaze target positions on models’ surfaces. For this purpose, a 3D collision ray needed to be deter- Measures. The usefulness of the gaze visualization techniques mined based on 2D screen-based gaze positions which are supplied was investigated in an online survey2 . Each technique was briefly by the eye tracker. The gaze ray was used to calculate and log its in- described with screenshots. Respondents were asked to rate their tersection with virtual objects on the precision level of a polygonal agreement to statements such as “Cone visualizations are useful for triangle on a model [M¨ ller and Trumbore 2005]. The processed o representing fixations in virtual environments.” The qualitative part data were stored in log files for post-analysis. of the survey collected comments about usefulness, improvements, and possible applications of the techniques. The presented visualization techniques were implemented in a pro- totype of a gaze analysis software tool: SVEETER (see Figure Procedure and Design. Group 1 had to answer the questions 5). It is based on XNA (for 3D scenes) and Windows Forms to based on brief textual and pictorial descriptions of the gaze visual- benefit from existing interface elements (e.g., buttons and menus). ization techniques provided in the online survey. For a better un- SVEETER offers a coherent framework for loading 3D scenes and derstanding of the new gaze visualizations, group 2 could use the corresponding gaze data logs, as well as deploying adapted gaze vi- SVEETER tool at our University. After welcoming each partici- sualizations techniques. Multiple views are integrated to look at a scene from different viewpoints. Context menus offer the possibil- 1 Rated on a Likert-scale from 1 (poor) to 5 (excellent). ity to apply certain options to each view, such as adapted rendering 2 The survey included 19 Likert scales, from 1 (do not agree at all) to options. The figures used throughout this paper to illustrate the dif- 5 (extremely agree), and 6 qualitative open questions using LimeSurvey ferent gaze visualization techniques were created with SVEETER. (Version 1.80+) 111
  4. 4. Figure 6: Agreement ratings from both groups with the corresponding standard deviations. pant of group 2, a short introduction about visual gaze analysis was 4 Conclusion and Future Work provided and the tool was briefly presented. Following this, sample gaze data for a 3D scene and a set of 9 predefined tasks were given In this paper, we presented a set of advanced gaze visualization to each individual. Thereby, the aim was systematical acquaintance techniques for investigating visual attention in 3D VEs. Three- with the techniques, not an efficiency evaluation. Tasks included, dimensional scan paths assist in examining sequences of fixations for example, to find out if particular parts of an object received high and saccades. Thereby, camera paths provide valuable information visual attention and if observers changed their viewpoints. After about how a user navigated through a scene and from which loca- completing the tasks and familiarizing themselves with the differ- tions objects have been observed. The models of interest timeline ent techniques, the visualization experts were asked to fill out the may help answering, for example, whether an object was viewed at online survey. On average, each session took about 45 minutes. a certain point in time or whether any cyclic viewing behavior ex- isted. Finally, three types of attentional maps were discussed to ex- Results. Means and standard deviation values are omitted in this amine how visual attention is distributed across a scene (projected paper, since none of the subjective results showed significant differ- heatmap), among 3D models (object-based attentional map), and ences between group 1 and group 2. In general, participants agreed across a model’s surface (surface-based heatmap). A combination that gaze visualization techniques facilitate eye tracking analysis. of these techniques was integrated in a toolkit for visual analysis The detailed results are shown in Figure 6. of gaze data (SVEETER). The survey results from eye tracking and visualization experts indicate the potential usefulness for various Scan paths are generally useful for studying gaze sequences. In application areas. this context, depicting camera paths is regarded important as well. Since visual gaze analysis for 3D VEs is still in an early stage, our Thus, a combination of camera and scan paths is found useful by techniques may serve as a solid foundation and provide a basis for both groups. In contrast, cones were considered only moderately further development. This includes representing data from multiple suitable for representing fixations. Instead, a simple combination users as well as developing and testing alternative techniques. of the spherical fixation representations with viewing directions and positions was preferred. While the motivation for individually de- picting fixations and saccades was not evident to everybody, it was References regarded useful in the overall rating. DACHSELT, R., AND W EILAND , M. 2006. TimeZoom: a flexible Besides limiting scan and camera paths temporally via the MOI detail and context timeline. In CHI ’06: Extended abstracts on timeline, additional filtering functionality was requested. Partici- Human factors in computing systems, ACM, 682–687. pants agreed that the MOI timeline helps to detect gaze patterns. D UCHOWSKI , A. T., S HIVASHANKARAIAH , V., R AWLS , T., Group 2 showed great interest in the MOI timeline when testing G RAMOPADHYE , A. K., M ELLOY, B., AND K ANKI , B. 2000. SVEETER. Both groups agreed that a zoomable user interface is Binocular eye tracking in virtual reality for inspection training. convenient for the timeline. Filtering objects of interest in the time- In ETRA ’00, ACM, 89–96. line was rated beneficial. Suggested improvements for the time- line included substituting color identification with iconic images L ESSING , S., AND L INGE , L. 2002. IICap: A new environment for and improved suitability for scenes with many objects by grouping eye tracking data analysis. Master’s thesis. University of Lund, them dynamically or individually. Sweden. The SVEETER tool was considered highly useful, combining scan ¨ M OLLER , T., AND T RUMBORE , B. 2005. Fast, minimum storage paths, attentional maps, and the MOI timeline. Thus, for evaluat- ray/triangle intersection. In SIGGRAPH ’05: ACM SIGGRAPH ing eye tracking studies in 3D VEs, interviewees could imagine to 2005 Courses, ACM. use SVEETER. Multiple views are rated practical together with the R AMLOLL , R., T REPAGNIER , C., S EBRECHTS , M., AND ability to assign different visualizations to each view. We observed B EEDASY, J. 2004. Gaze data visualization tools: opportunities that a combination of different techniques was often employed by and challenges. Eighth International Conference on Information users testing SVEETER (group 2). Both scan paths and surface- Visualisation (July), 173–180. based maps were, for example, used to investigate observation pat- terns on a model. Participants frequently faded out objects to gain a W OODING , D. S. 2002. Fixation maps: quantifying eye-movement better overview. In addition, we noticed that the MOI timeline was traces. In ETRA ’02, ACM, 31–36. often used instead of scan paths. 112

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