Aerospace & Defense Technology - June 2021 - 32

Tech Briefs
Quantifying Eye Movement Trajectory Similarity for Use in
Human Performance Experiments in Intelligence,
Surveillance, and Reconnaissance (ISR) Research
Using ScanMatch, a Matlab software package, to determine the similarity of two strings of eye tracking
data provides useful background and recommendations for potential applications in Intelligence,
Surveillance, and Reconnaissance (ISR) research.
Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio
T
he performance of persons who watch
surveillance videos, either in real-time
or recordings, can vary with their level of
expertise. It is reasonable to suppose that
some of the performance differences
might be due, at least in part, to the way
experts scan a visual scene versus the way
novices might scan the same scene. For
example, experts might be more systematic
or efficient in the way they scan a
scene compared to novices. Even within
the same person, video surveillance performance
can vary with factors such as fatigue.
Again, differences in the way their
eyes scan a scene might account for some
of the differences. Full Motion Video
(FMV) " Eyes-on " intelligence analysts, in
particular, actively scan video scenes for
items of interest for long periods of time.
To better understand the characteristics
of scanning behavior of Intelligence,
Surveillance, and Reconnaissance (ISR)
analysts, it is important to track eye
movement characteristics. It is relatively
simple to model eye characteristics over
time such as pupil dilation or eyelid
opening over time. It is also common to
characterize different types of eye movement
over time.
However, when it comes to making
comparisons between two sequences of
eye data, it is much more challenging
and by default, most commercial eye
tracking systems do not come equipped
with analytic measures of comparing traSample
Plot A
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jectory morphologies. However, there
are a variety of applications where this
comparison can be useful to ISR research:
* Comparing eye scanning movements
before and after critical events, such as
detecting a target;
* Comparing scanpaths between two different
analysts;
* Comparing scanpaths of analysts over
time (e.g. early in the day vs. later in an
analyst's shift);
* Comparing scanpaths across different
surveillance tasks;
* Comparing scanpaths in simple versus
complex surveillance scenarios.
This research explores some common
metrics for quantifying the similarity between
two eye movement scanpaths, with
an emphasis on the Matlab toolbox ScanMatch.
ScanMatch is a Matlab package
that computes a similarity score between
two scanpaths. Both experiments involving
pseudodata with known conclusions
and experimental data from Piaseki
(2016) were analyzed using ScanMatch.
In research on human analyst performance
in ISR tasks, it is useful to collect eye
tracking metrics of analysts as they perform
search operations. In particular, information
about fixations and saccades
(i.e., the jumps from one fixation to another),
has been useful for yielding information
regarding:
* Workload;
* Fatigue;
Sample Plot B
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Three sample plots of scanpath data. The leftmost image (Plot A) is highly similar to the scanpath of Plot B, but
is highly dissimilar from the scanpath of Plot C. All 3 plots are made with an identical number of raw gaze points.
32
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ToC
Sample Plot C
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* Attention; and even
* Inattention blindness.
Fixation and saccade locations can
serve as markers for attention because
where an analyst is looking on the
screen is often highly correlated with
what he or she is attending to. However,
highly correlated does not mean perfectly
correlated with conscious attention,
as a well-known inattention blindness
study by Drew, Vo, and Wolfe
(2013) demonstrates. In their study, radiologists
examining an X-ray often fixated
on a gorilla figure embedded in the
X-ray and made repeated backtracks or
re-fixations to it but did not consciously
notice the anomaly. Inattention blindness
and associated re-fixation and
backtrack patterns are important in understanding
ISR analyst performance.
Among the rich variables that are collected
in eye tracking data, one that
centers on the temporal aspects of fixation
patterns but whose analysis is frequently
neglected due to its complexity,
is the scanpath. A scanpath is defined
by the temporal sequence of point-bypoint
(x,y) screen coordinates of where
a person is looking on the screen. The
accompanying figure shows three example
notional scanpaths, although the
temporal direction is not shown. At a
minimum, scanpaths encompass at
least one full fixation-saccade-fixation
sequence. Clearly, scanpaths capture
the fixation, re-fixation, and backtrack
patterns that reveal an analyst's attention,
conscious or otherwise.
This work was done by Dr. Mary E. Frame
for the Air Force Research Laboratory. For
more information, download the Technical
Support Package (free white paper) at
www.aerodefensetech.com/tsp under
the DAQ Testing & Sensors category.
AFRL-0303
Aerospace & Defense Technology, June 2021
http://www.aerodefensetech.com/tsp http://www.aerodefensetech.com http://www.abpi.net/ntbpdfclicks/l.php?202106ADTNAV

Aerospace & Defense Technology - June 2021

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