Conference PaperPDF Available

Evaluating Quality of Dispersion Based Fixation Detection Algorithm

November 2014

Conference: 29th International Symposium on Computer and Information Sciences
At: Kraków

Authors:

Silesian University of Technology

Information hidden in the eye movement signal can be a valuable source of knowledge about a human mind. This information is commonly used in multiple fields of interests like psychology, medicine, business, advertising or even software developing. The proper analysis of the eye movement signal requires its elements to be extracted. The most important ones are fixations-moments when eyes are almost stable and the brain is acquiring information about the scene. There were several algorithms, aiming at detecting fixations, developed. The studies presented in this paper focused one of the most common dispersion based algorithm-I-DT one. The various ways of evaluating its results were analyzed and compared. Some extensions in this algorithm were made as well.

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Evaluating quality of dispersion based ﬁxation

detection algorithm

Katarzyna Hare˙

zlak and Paweł Kasprowski

Abstract Information hidden in the eye movement signal can be a valuable source

of knowledge about a human mind. This information is commonly used in multiple

ﬁelds of interests like psychology, medicine, business, advertising or even software

developing. The proper analysis of the eye movement signal requires its elements

to be extracted. The most important ones are ﬁxations - moments when eyes are

almost stable and the brain is acquiring information about the scene. There were

several algorithms, aiming at detecting ﬁxations, developed. The studies presented

in this paper focused one of the most common dispersion based algorithm - I-DT

one. The various ways of evaluating its results were analyzed and compared. Some

extensions in this algorithm were made as well.

1 Introduction

Human eyes play important role in the interpersonal communication and gathering

knowledge regarding surrounding world. The desire to understand this learning pro-

cess leads to asking many questions: What is a subject looking at? What does one

see looking at a given point? Did one ﬁnd searched information? What kind of in-

formation was gained when looking at a particular area? Is one looking at expected

point of regard? Finding answers to those and other questions is an important task

in many ﬁelds of interests like psychology, medicine, business, advertising or soft-

ware developing. This need is reﬂected in current research areas, among which the

Katarzyna Hare˙

zlak

Silesian University of Technology, Institute of Informatics, Gliwice, Poland e-mail:

katarzyna.harezlak@polsl.pl

Paweł Kasprowski

Silesian University of Technology, Institute of Informatics, Gliwice, Poland e-mail:

kasprowski@polsl.pl

This is a pre-print. The original version was published by

Springer International Publishing Switzerland,

Information Sciences and Systems 2014, pp 97-104, DOI 10.1007/978-3-319-09465-6_11

2 Katarzyna Hare˙

zlak and Paweł Kasprowski

cognizance of an eye movement signal has a signiﬁcant place, because information

hidden in this signal can be a valuable source of knowledge.

Studies conducted in this ﬁeld resulted in distinguishing few components of the

signal. Its fundamental unit is a ﬁxation when point-of-gaze remains within a small

area for a given time. Fixations are interlaced with saccades - quick movements

made to reach another point-of-regard [1, 5, 6, 10, 15]. The example interpretations

of ﬁxations and saccades features, in terms of their usability in a cognitive process of

human behaviors, were presented in the [9]. These guidelines show how important

is a precise separation of these parts from an eye movement signal.

However, deeper analysis of a ﬁxation reveals, within it, another types of move-

ment: tremors, microsaccades and drifts [6]. A quality of measurements is also an

important issue. For this reason the ﬁxation cannot be thought as one point and some

additional measures have to be involved like e.g. a size of spatial dispersion between

points in the ﬁxation. Additionally, an eye movement signal differs in a character-

istic for a particular subject and task, which makes the identiﬁcation of ﬁxations a

complex task, still being solved by researches.

There are several algorithms developed for identifying ﬁxations and saccades.

Among them Dispersion-Based and Velocity-Based algorithms are the most popu-

lar ones [11, 12]. The ﬁrst group of methods identiﬁes ﬁxations analyzing distances

of the consecutive points. Group of points satisfying the condition deﬁned by a given

dispersion threshold is treated as a ﬁxation. This threshold can refer to various mea-

sures - a distance between any two points, the largest distance between points in a

ﬁxation or the largest distance from the center of a ﬁxation to one of constituting its

points [11, 12]. The most often analyzed algorithms in this group are I-DT (Disper-

sion Threshold Identiﬁcation) and MST (Minimum Spanning Tree Identiﬁcation)

[4, 7, 11, 12].

The studies of points velocity entitled development of algorithms separating ﬁx-

ations and saccades points, taking their point-to-point velocities into account. Well-

known representatives of these methods are I-VT (Velocity Threshold Identiﬁcation)

and HHM (Hidden Markov Model Identiﬁcation) methods [4, 7, 11, 12].

The common goal of these algorithms is to extract sets of ﬁxations and saccades

facilitating the interpretation of eye movement signals. Fixations are usually con-

sidered to have a duration of at least 150 msec. [5, 11, 12], nevertheless discussions

concerning a ﬁxation duration in terms of performed tasks can be found [10, 14].

However, there are other factors inﬂuencing the analyzed measure. The outcome de-

pends on input parameters, which are different kinds of thresholds. Various range of

these parameters values can lead to diversity of results i.e. a number and duration of

ﬁxations and saccades. For this reason several experiments were conducted to check

these parameters impact on the obtained results [1, 2, 4, 11].

Although the aforementioned studies let for drawing interesting conclusions,

they did not exhaust the topic. The aim of the research is to continue the analy-

sis of the I-DT dispersion based algorithm, which is claimed to be robust one [1, 11]

taking ﬁxation identiﬁcation into account.

Evaluating quality of dispersion based ﬁxation detection algorithm 3

2 Algorithm description

The presented studies involved, as a basis, classic I-DT algorithm [11], converting

eye movements signal into list of ﬁxations in two steps. In the ﬁrst step, each gaze

point is classiﬁed as a ﬁxation (F) or a saccade (S). The point is considered to be a

part of a ﬁxation when some amount of previous points (called window) are closer

than a predeﬁned threshold. There are different possibilities how to measure the

distance [11, 12, 2] but the most common is the maximal distance between any two

points in the window.

The second step is a consolidation of F-points, laying one by one, into ﬁxa-

tions. Fixations with length lower than another predeﬁned threshold (called later

minLength) are removed. The output of the algorithm is a set of ﬁxations. Every

ﬁxation has four parameters: start,length and position with two values: x,y. Pa-

rameters xand yare typically calculated as mean of all points in a ﬁxation although

there are other possibilities (like e.g. median).

The algorithm used in this research extended a classic I-DT algorithm by intro-

ducing an additional step that makes it more ﬂexible for low quality data (similar to

[4]). The classic I-DT algorithm builds ﬁxations list in step 2 based on minLength

threshold - that is forms ﬁxations from ﬁxation points courses that are longer than

that threshold. In our modiﬁed algorithm every course of F points is used to build

a ﬁxation. In additional step 3 we calculate spatial and temporal distances between

every two neighboring ﬁxations. If both distances are below the threshold the two

ﬁxations are merged into one. Only after this step minLength threshold is applied to

every ﬁxation.

Dispersion threshold algorithm

Input: list of (x,y) points

Step 1

Classify each point as a ﬁxation (F) or a saccade (S) based on history:

Point is a ﬁxation when max distance among window previous points is less than threshold

Step 2

Build ﬁxations from groups of neighboring F points:

For every course of at least two subsequent F points

build ﬁxation(start,length,x,y) where x and y are average values of x and y

among all points in a ﬁxation.

Step 3

Merge neighboring ﬁxations:

For every two subsequent ﬁxations:

if temporal gap (saccade length) between them is less than tgapThres

and spatial gap (Euclidean distance between them) is less than sgapThres

then merge these two ﬁxations into one.

Step 4

Remove too short ﬁxations:

For every ﬁxation:

if a ﬁxation length is less than minLength remove the ﬁxation.

Output/result: list of ﬁxations

4 Katarzyna Hare˙

zlak and Paweł Kasprowski

The additional ﬁxations merging step should make the algorithm more robust to

artifacts - sudden and unexpected changes of measured eye position due to imper-

fection of eye tracker algorithms and noise. Artifacts removal in an eye movement

signal is not simple, especially when one does not want to affect parts of a cor-

rect signal. Similar setup, but for I-VT algorithm and restricted only for trackloss

situation (when there is no data available) was presented in [7].

3 Quality evaluation

The main problem for every ﬁxation detection algorithm is how to check whether

it works correctly. The ground truth is in most cases not possible to determine as

we don’t know the correct positions of ﬁxations and saccades in an eye movement

signal. There are multiple ways to solve the problem. [7] and [13] compared ﬁxa-

tions detected by their algorithm to ﬁxations detected by manual inspection made

by experienced users. [2] used somewhat arguable similarity criterion: if different

algorithms give similar data the data is considered to be reliable. The other possible

criterion proposed in [16] is calculation of so called nearest neighbour index (NNI)

for every ﬁxation.

In this study we used a specially designed stimulus with a point jumping over

a screen. The task of a person being tested was to follow, with eyes, the point,

which was changing its location in speciﬁc moments of time. The fact that ﬁxation

placements were known, gave us opportunity to create an estimated correct sequence

of ﬁxations (later called template sequence) and compare the results of the algorithm

to this sequence.

Experiment. There were altogether 24 participants taking part in the experi-

ments, for which 40 recordings were registered using the stimulus described earlier.

To obtain meaningful results there were only samples with accuracy error, estimated

during calibration step, lower than 1.5 deg. chosen. Every stimulus presentation con-

sisted of 21 points evenly distributed over the whole screen. The point was displayed

in every location for about 3 seconds. Eye movements were recorded with the eye

tracker using single web camera with USB 2.0 interface, the sampling frequency

was 20Hz.

Methodology. The algorithm presented in section 2 was used to produce se-

quences of ﬁxations for every sample. The algorithm was started with different val-

ues of threshold, spatial gap (sgapThres) and temporal gap (tgapThres). Parameters

window and minLength were set to 5 as it seems to be a reasonable choice according

to the literature. Assessment of the obtained results - sequences of ﬁxations gener-

ated by the algorithm with various values of three parameters - threshold,sgapThres

and tgapThres - was done using several metrics described in Table 1.

To provide the correctness of the algorithm, metrics described in Table 1, were

calculated for every set of parameters and compared to metrics calculated for tem-

plate ﬁxation sequence. For that sequence the values were: AFN=21, AFD=71.29,

ASN=20 and ASA=19.2.

Evaluating quality of dispersion based ﬁxation detection algorithm 5

AFN - AverageFixation Number Amount of ﬁxations divided by a number of elements in an

analyzed set of values

AFD - AverageFixation Duration A value of summarized ﬁxations length, measured in milli-

seconds, divided by a number of elements in an analyzed set

of values

ASN - AverageSaccades Number Amount of saccades divided by a number of elements

in an analyzed set of values

ASA - AverageSaccades Amplitude Sum of distances between every two ﬁxations divided

by a number of elements in an analyzed set of values.

Table 1 Metrics used to describe sequence of ﬁxations

Additionally, there were several metrics calculated that directly measured dif-

ference between a given sequence and the template sequence. These metrics are

presented in Table 2. More information about metrics used may be found in [3] and

[4].

FQnS - Fixation Quantitative Score Percentage of points included into a ﬁxation, for which

distance from a stimulus position is less than third part of

a last saccade amplitude.

FQlS - Fixation Qualitative Score A value of summarized distances between calculated

ﬁxations and stimuli positions divided by a number

of stimuli positions presented.

SQnS - Saccade Quantitative Score A value of summarized saccades amplitudes divided by

summarized distances between stimuli positions presented

LevDist - Levenstein Distance A Levenshtein distance between a calculated sequence of

ﬁxations and the template sequence

Table 2 Metrics used to calculate the similarity to the template

4 Results

At the beginning of the studies an inﬂuence of one parameter - threshold - on the re-

sults provided by the algorithm was checked. Its value initially set to 0.5 degree, was

incremented by 0.05 up to the value of 10 degrees. The sgapThres and tgapThres

were constant with value 0. As a main metrics, assessing a quality of the result,

Levenstein distance (LevDist) was chosen. The analysis of its values revealed that

there are two threshold ranges having meaningful inﬂuence on them. Low thresh-

olds values caused splitting a ﬁxation, when an amplitude of eyes trembling during

a ﬁxation was higher than a given threshold. For the described studies it was a case

when threshold was lower than 2.5 deg, which can be observed in the Fig 1 (left

side). Above this value the stabilization of LevDist was noticed as far as the thresh-

old reached the second range, values higher than 8 degrees. Deﬁning this parameter

6 Katarzyna Hare˙

zlak and Paweł Kasprowski

on such high level resulted in merging ﬁxation and points of neighboring saccades,

which entitled increasing of LevDist values.

To check the correctness of these ﬁndings they were compared to values obtained

for FQnS metrics, which are presented in the Fig 1 (right side). The symmetric shape

of charts suggested the strong correlation between elements of both sets. This cor-

relation turned to be full one with the coefﬁcient equal -0.967. Similar relationship

was found between LevDist and ANF metrics. In this case the correlation coefﬁcient

was -0.981 The analysis of the obtained results regarded a duration of a ﬁxation

Fig. 1 Charts presenting the average values of LevDist (left side) and of FQnS (right side) metrics

for various thresholds

as well. As it was mentioned earlier, the template of a ﬁxation length was deﬁned

as time period, when a stimulus was displayed in one position (3565 msec.). It is

well known problem that this value cannot be reached because of the fact that, when

a point on the screen changes its position, it takes some time for the human brain

to react and to initiate an eye movement. For this reason duration of a measured

ﬁxation will never be ideal.

In Fig 2 it can be noticed that in case of low threshold values, ﬁxations found

by the algorithm, feature by a short duration. It is another conﬁrmation, that setting

threshold in this scope can result in splitting one ﬁxation into few small ones. It can

also be observed that extending threshold to 10 degrees makes the values of AFD

metrics to be closer to the ideal value. However it cannot be assumed that for high

threshold better result are obtained. As it was discussed above, this occurrence is

the effect of attaching to a ﬁxation points, which in fact do not belong to it. This

proves that the correctness of the found ﬁxations should be ensured by more than

one metrics.

Fig. 2 Chart presenting the average values of AFD metric for various thresholds

Evaluating quality of dispersion based ﬁxation detection algorithm 7

Last metrics taken during the studies into account were ASA and SQnS one. Com-

paring these two sets of values it turned out that their correlation is signiﬁcant (with

coefﬁcient equal = -0.542) but not in such high level like in earlier described cases.

Both of metrics involve an amplitude of saccades but using it in slightly different

way. First of them is strongly dependent on the number of ﬁxations deﬁned by the

algorithm, which determines the number of saccades (ASN). The second metric is

the ratio being the sum of found ﬁxations amplitudes divided by the sum of saccades

amplitudes existing in the template.

These various approaches entitle the differences in the results. In case of small

threshold values a signal is divided into small ﬁxations, between which saccades

with amplitudes smaller the expected are deﬁned. The sum of this amplitudes di-

vided by the number of saccades provides low ASA value (Fig 3 (left side)). In case

of the second metric (SQnS) the sum of such determined amplitudes can be in fact

almost equal to the sum of bigger amplitudes of the smaller set of saccades. This is

why the SQnS value are almost stable, up to the threshold value of 8 degree, when

the problem of merging a ﬁxation with some points of neighboring saccades occurs

(Fig 3 (right side)).

Fig. 3 Charts presenting the average values of ASA (left side) and SQnS (right side) metrics for

various thresholds

5 Conclusion

The main goal of the research presented in the paper was to check how various

parameters of well-known I-DT algorithm, used for extracting the set of ﬁxations

from the eye movement signal can inﬂuence the obtained results. The experiments

based on the specially designed stimulus with a point jumping over a screen. The

known positions of the point were used as the reference template. Owing to that

it was possible to determine, which ranges of parameters values provide reliable

outcomes. It especially was visible for a main threshold parameter. The assessment

was supported by the usage of various metric. Convergent results of independently

calculated metrics values conﬁrmed correctness of the algorithm. The results for the

other parameters were not so unambiguous and need further studies. The average

best LevDist for I-DT algorithm without additional merging step (i.e. tgapThres and

8 Katarzyna Hare˙

zlak and Paweł Kasprowski

sgapThres equal to 0) was 3.175. The average LevDist for the algorithm with the ad-

ditional step was 2.85. Merging algorithm improved results in 28% of cases. How-

ever, these results were achieved for different values of sgapThres and tgapThres

optimized separately for every ﬁle. It was impossible to ﬁnd one universal set of

sgapThres and sgapThres values that on average for every ﬁle gave results better

than without merging step (sgapThres=0, tgapThres=0). Nevertheless, it was ob-

served that for the given threshold parameter value higher tgapThres thresholds im-

proved the LevDist decreasing its value.

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INFORM SCIENCES

Katarzyna Harezlak

Eye movement is one of the key biological signals through which further analysis may reveal substantial information enabling greater understanding the biology of the brain and its mechanisms. Several methods for such signal processing have been developed, however new solutions are being continuously sought. This paper presents analysis of one of the main eye movement components – fixation – by usage of nonlinear time series methods. This analysis, aimed at determining the existence of chaotic behaviour, indicated by many biological systems, was based on an experiment utilising ’jumping point’ stimulus. 29 stimuli were used – presented for 3 secs in different screen positions. 24 subjects participated in the experiment consisting of two sessions conducted with a two–month interval; thus the experimental dataset included 48 recordings. The first derivative of the horizontal positions of eye movement coordinates registered during a fixation served as the time series used for reconstruction of eye movement dynamics. The analysis was performed by means of the Largest Lyapunov Exponent. Its values were studied in various time scopes of a fixation duration. A positive averaged value of this exponent, indicating chaotic behaviour, was observed for the first 200 points in the case of all studied time series. In the remining of the analysed scopes negative average exponent values were shown, however, for a number of users the eye movement signal behaviour was changing from convergent to chaotic and conversely.

The effect of fixational eye movements on fixation identification with a dispersion-based fixation detection algorithm

Article

Full-text available

Apr 2009

Gaze data of 31 participants of a memory recall experiment was analyzed and the I-DT dispersion based algorithm of Salvucci and Goldberg (2000) was used to identify fixations. It was found that individuals differ considerably with regard to the stability of eye gaze and that fixational eye movements affect the accuracy of fixation identification and the optimum dispersion threshold. It was also found that fixation radius and the distance between the points in a fixation that are the furthest apart are the most reliable metrics for a dispersion-based fixation identification algorithm. Finally, it is argued that the correct setting of dispersion threshold is of utmost importance, especially if the participants are not homogeneous with regard to gaze stability.

First Eye Movement Verification and Identification Competition at BTAS 2012

Conference Paper

Full-text available

Sep 2012

This paper presents the results of the first eye movement verification and identification competition. The work provides background, discusses previous research, and describes the datasets and methods used in the competition. The results highlight the importance of very careful eye positional data capture to ensure meaningfulness of identification outcomes. The discussion about the metrics and scores that can assist in evaluation of the captured data quality is provided. Best identification results varied in the range from 58.6% to 97.7% depending on the dataset and methods employed for the identification. Additionally, this work discusses possible future directions of research in the eye movement-based biometrics domain.

Eye tracking in human-computer interaction and usability research: Current status and future prospects

Chapter

Full-text available

Jan 2006

Eye-movement tracking is a method that is increasingly being employed to study usability issues in HCI contexts. The objectives of the present chapter are threefold. First, we introduce the reader to the basics of eye-movement technology, and also present key aspects of practical guidance to those who might be interested in using eye tracking in HCI research, whether in usability-evaluation studies, or for capturing people's eye movements as an input mechanism to drive system interaction. Second, we examine various ways in which eye movements can be systematically measured to examine interface usability. We illustrate the advantages of a range of different eye-movement metrics with reference to state-of-the-art usability research. Third, we discuss the various opportunities for eye-movement studies in future HCI research, and detail some of the challenges that need to be overcome to enable effective application of the technique in studying the complexities of advanced interactive-system use.

Eye fixations identification based on statistical analysis - Case study

Conference Paper

Full-text available

Jul 2010

Eye movement is the most simple and repetitive movement that enable humans to interact with the environment. The common daily activities, such as watching television or reading a book, involve this natural activity which consists of rapidly shifting our gaze from one region to another. The identification of the main components of eye movement during visual exploration such as fixations and saccades, is the objective of the analysis of eye movements in various contexts ranging from basic neuro sciences and visual sciences to virtual reality interactions and robotics. However, many of the algorithms that detect fixations present a number of problems. In this article, we present a new fixation identification algorithm based on the analysis of variance and F-test. We present the new algorithm and we compare it with the common fixations algorithm based on dispersion. To demonstrate the performance of our approach we tested the algorithm in a group of healthy subjects.

Standardization of Automated Analyses of Oculomotor Fixation and Saccadic Behaviors

Article

Full-text available

Dec 2010

In an effort toward standardization, this paper evaluates the performance of five eye-movement classification algorithms in terms of their assessment of oculomotor fixation and saccadic behavior. The results indicate that performance of these five commonly used algorithms vary dramatically, even in the case of a simple stimulus-evoked task using a single, common threshold value. The important contributions of this paper are: evaluation and comparison of performance of five algorithms to classify specific oculomotor behavior; introduction and comparison of new standardized scores to provide more reliable classification performance; logic for a reasonable threshold-value selection for any eye-movement classification algorithm based on the standardized scores; and logic for establishing a criterion-based baseline for performance comparison between any eye-movement classification algorithms. Proposed techniques enable efficient and objective clinical applications providing means to assure meaningful automated eye-movement classification.

Fixation-identification in dynamic scenes: Comparing an automated algorithm to manual coding

Conference Paper

Full-text available

Aug 2008

Video-based eye trackers produce an output video showing where a subject is looking, the subject's point-of-regard (POR), for each frame of a video of the scene. Fixation-identification algorithms simplify the long list of POR data into a more manageable set of data, especially for further analysis, by grouping PORs into fixations. Most current fixation-identification algorithms assume that the POR data are defined in static two-dimensional scene images and only use these raw POR data to identify fixations. The applicability of these algorithms to gaze data in dynamic scene videos is largely unexplored. We implemented a simple velocity-based, duration-sensitive fixation-identification algorithm and compared its performance to results obtained by three experienced users manually coding the eye tracking data displayed within the scene video such that these manual coders had knowledge of the scene motion. We performed this comparison for eye tracking data collected during two different tasks involving different types of scene motion. These two tasks included a subject walking around a building for about 100 seconds (Task 1) and a seated subject viewing a computer animation (approximately 90 seconds long, Task 2). It took our manual coders on average 75 minutes (stdev = 28) and 80 minutes (17) to code results from the first and second tasks, respectively. The automatic fixation-identification algorithm, implemented in MATLAB and run on an Apple 2.16 GHz MacBook, produced results in 0.26 seconds for Task 1 and 0.21 seconds for Task 2. For the first task (walking), the average percent difference among the three human manual coders was 9% (3.5) and the average percent difference between the automatically generated results and the three coders was 11% (2.0). For the second task (animation), the average percent difference among the three human coders was 4% (0.75) and the average percent difference between the automatically generated results and the three coders was 5% (0.9).

Identifying fixations and saccades in eye-tracking protocols

Conference Paper

Full-text available

Jan 2000

The process of fixation identification—separating and labeling fixations and saccades in eye-tracking protocols—is an essential part of eye-movement data analysis and can have a dramatic impact on higher-level analyses. However, algorithms for performing fixation identification are often described informally and rarely compared in a meaningful way. In this paper we propose a taxonomy of fixation identification algorithms that classifies algorithms in terms of how they utilize spatial and temporal information in eye-tracking protocols. Using this taxonomy, we describe five algorithms that are representative of different classes in the taxonomy and are based on commonly employed techniques. We then evaluate and compare these algorithms with respect to a number of qualitative characteristics. The results of these comparisons offer interesting implications for the use of the various algorithms in future work.

The Incomplete Fixation Measure

Conference Paper

Full-text available

Jan 2008

In this paper we evaluate several of the most popular algorithms for segmenting fixations from saccades by testing these algorithms on the scanning patterns of toddlers. We show that by changing the pa- rameters of these algorithms we change the reported fixation dura- tions in a systematic fashion. However, we also show how choices in analysis can lead to very different interpretations of the same eye-tracking data. Methods for reconciling the disparate results of different algorithms as well as suggestions for the use of fixation identification algorithms in analysis, are presented. CR Categories: J.4 (Computer Applications): Social and Behav- ioral Sciences—Psychology

ASTEF: A simple tool for examining fixations

Article

Full-text available

May 2008

In human factors and ergonomics research, the analysis of eye movements has gained popularity as a method for obtaining information concerning the operator's cognitive strategies and for drawing inferences about the cognitive state of an individual. For example, recent studies have shown that the distribution of eye fixations is sensitive to variations in mental workload---dispersed when workload is high, and clustered when workload is low. Spatial statistics algorithms can be used to obtain information about the type of distribution and can be applied over fixations recorded during small epochs of time to assess online changes in the level of mental load experienced by the individuals. In order to ease the computation of the statistical index and to encourage research on the spatial properties of visual scanning, A Simple Tool for Examining Fixations has been developed. The software application implements functions for fixation visualization, management, and analysis, and includes a tool for fixation identification from raw gaze point data. Updated information can be obtained online at www.astef.info, where the installation package is freely downloadable.

Bayesian online clustering of eye movement data

Article

Mar 2012

The task of automatically tracking the visual attention in dynamic visual scenes is highly challenging. To approach it, we propose a Bayesian online learning algorithm. As the visual scene changes and new objects appear, based on a mixture model, the algorithm can identify and tell visual saccades (transitions) from visual fixation clusters (regions of interest). The approach is evaluated on real-world data, collected from eye-tracking experiments in driving sessions.

Evaluating Quality of Dispersion Based Fixation Detection Algorithm

Abstract

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