EyeSee3D 2.0: model-based real-time analysis of mobile eye-tracking in
static and dynamic three-dimensional scenes
Multimodal gaze
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Pfeiffer, Thies
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Renner, Patrick
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Pfeiffer-Leßmann, Nadine
Proceedings of the 2016 Symposium on Eye Tracking Research &
Applications
2016-03-14
p.189-196
© Copyright 2016 ACM
Summary: With the launch of ultra-portable systems, mobile eye tracking finally has
the potential to become mainstream. While eye movements on their own can
already be used to identify human activities, such as reading or walking,
linking eye movements to objects in the environment provides even deeper
insights into human cognitive processing.
We present a model-based approach for the identification of fixated objects
in three-dimensional environments. For evaluation, we compare the automatic
labelling of fixations with those performed by human annotators. In addition to
that, we show how the approach can be extended to support moving targets, such
as individual limbs or faces of human interaction partners. The approach also
scales to studies using multiple mobile eye-tracking systems in parallel.
The developed system supports real-time attentive systems that make use of
eye tracking as means for indirect or direct human-computer interaction as well
as off-line analysis for basic research purposes and usability studies.
EyeSee3D: a low-cost approach for analyzing mobile 3D eye tracking data
using computer vision and augmented reality technology
Mobile eye tracking & applications
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Pfeiffer, Thies
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Renner, Patrick
Proceedings of the 2014 Symposium on Eye Tracking Research &
Applications
2014-03-26
p.195-202
© Copyright 2014 ACM
Summary: For validly analyzing human visual attention, it is often necessary to
proceed from computer-based desktop set-ups to more natural real-world
settings. However, the resulting loss of control has to be counterbalanced by
increasing participant and/or item count. Together with the effort required to
manually annotate the gaze-cursor videos recorded with mobile eye trackers,
this renders many studies unfeasible.
We tackle this issue by minimizing the need for manual annotation of mobile
gaze data. Our approach combines geometric modelling with inexpensive 3D marker
tracking to align virtual proxies with the real-world objects. This allows us
to classify fixations on objects of interest automatically while supporting a
completely free moving participant.
The paper presents the EyeSee3D method as well as a comparison of an
expensive outside-in (external cameras) and a low-cost inside-out (scene
camera) tracking of the eye-tracker's position. The EyeSee3D approach is
evaluated comparing the results from automatic and manual classification of
fixation targets, which raises old problems of annotation validity in a modern
context.
Model-based acquisition and analysis of multimodal interactions for
improving human-robot interaction
Demo/video session
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Renner, Patrick
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Pfeiffer, Thies
Proceedings of the 2014 Symposium on Eye Tracking Research &
Applications
2014-03-26
p.361-362
© Copyright 2014 ACM
Summary: For solving complex tasks cooperatively in close interaction with robots,
they need to understand natural human communication. To achieve this, robots
could benefit from a deeper understanding of the processes that humans use for
successful communication. Such skills can be studied by investigating human
face-to-face interactions in complex tasks. In our work the focus lies on
shared-space interactions in a path planning task and thus 3D gaze directions
and hand movements are of particular interest.
However, the analysis of gaze and gestures is a time-consuming task:
Usually, manual annotation of the eye tracker's scene camera video is necessary
in a frame-by-frame manner. To tackle this issue, based on the EyeSee3D method,
an automatic approach for annotating interactions is presented: A combination
of geometric modeling and 3D marker tracking serves to align real world stimuli
with virtual proxies. This is done based on the scene camera images of the
mobile eye tracker alone. In addition to the EyeSee3D approach, face detection
is used to automatically detect fixations on the interlocutor. For the
acquisition of the gestures, an optical marker tracking system is integrated
and fused in the multimodal representation of the communicative situation.
EyeSee3D: a low-cost approach for analyzing mobile 3D eye tracking data
using computer vision and augmented reality technology
Demo/video session
/
Pfeiffer, Thies
/
Renner, Patrick
Proceedings of the 2014 Symposium on Eye Tracking Research &
Applications
2014-03-26
p.367-374
© Copyright 2014 ACM
Summary: For validly analyzing human visual attention, it is often necessary to
proceed from computer-based desktop set-ups to more natural real-world
settings. However, the resulting loss of control has to be counterbalanced by
increasing participant and/or item count. Together with the effort required to
manually annotate the gaze-cursor videos recorded with mobile eye trackers,
this renders many studies unfeasible.
We tackle this issue by minimizing the need for manual annotation of mobile
gaze data. Our approach combines geometric modelling with inexpensive 3D marker
tracking to align virtual proxies with the real-world objects. This allows us
to classify fixations on objects of interest automatically while supporting a
completely free moving participant.
The paper presents the EyeSee3D method as well as a comparison of an
expensive outside-in (external cameras) and a low-cost inside-out (scene
camera) tracking of the eye-tracker's position. The EyeSee3D approach is
evaluated comparing the results from automatic and manual classification of
fixation targets, which raises old problems of annotation validity in a modern
context.
