Towards an embodied semantic fovea: Semantic 3D scene reconstruction
from ego-centric eye-tracker videos
from ego-centric eye-tracker videos
File(s)
Working paper
Author(s)
Li, Mickey
Songur, Noyan
Orlov, Pavel
Leutenegger, Stefan
Faisal, A Aldo
Type
Working Paper
Abstract
Incorporating the physical environment is essential for a complete
understanding of human behavior in unconstrained every-day tasks. This is
especially important in ego-centric tasks where obtaining 3 dimensional
information is both limiting and challenging with the current 2D video analysis
methods proving insufficient. Here we demonstrate a proof-of-concept system
which provides real-time 3D mapping and semantic labeling of the local
environment from an ego-centric RGB-D video-stream with 3D gaze point
estimation from head mounted eye tracking glasses. We augment existing work in
Semantic Simultaneous Localization And Mapping (Semantic SLAM) with collected
gaze vectors. Our system can then find and track objects both inside and
outside the user field-of-view in 3D from multiple perspectives with reasonable
accuracy. We validate our concept by producing a semantic map from images of
the NYUv2 dataset while simultaneously estimating gaze position and gaze
classes from recorded gaze data of the dataset images.
understanding of human behavior in unconstrained every-day tasks. This is
especially important in ego-centric tasks where obtaining 3 dimensional
information is both limiting and challenging with the current 2D video analysis
methods proving insufficient. Here we demonstrate a proof-of-concept system
which provides real-time 3D mapping and semantic labeling of the local
environment from an ego-centric RGB-D video-stream with 3D gaze point
estimation from head mounted eye tracking glasses. We augment existing work in
Semantic Simultaneous Localization And Mapping (Semantic SLAM) with collected
gaze vectors. Our system can then find and track objects both inside and
outside the user field-of-view in 3D from multiple perspectives with reasonable
accuracy. We validate our concept by producing a semantic map from images of
the NYUv2 dataset while simultaneously estimating gaze position and gaze
classes from recorded gaze data of the dataset images.
Date Issued
2018-07-27
Citation
2018
Identifier
http://arxiv.org/abs/1807.10561v1
Subjects
cs.CV
cs.CV
cs.HC
cs.RO