2,796 research outputs found
UnrealEgo: A New Dataset for Robust Egocentric 3D Human Motion Capture
We present UnrealEgo, i.e., a new large-scale naturalistic dataset for
egocentric 3D human pose estimation. UnrealEgo is based on an advanced concept
of eyeglasses equipped with two fisheye cameras that can be used in
unconstrained environments. We design their virtual prototype and attach them
to 3D human models for stereo view capture. We next generate a large corpus of
human motions. As a consequence, UnrealEgo is the first dataset to provide
in-the-wild stereo images with the largest variety of motions among existing
egocentric datasets. Furthermore, we propose a new benchmark method with a
simple but effective idea of devising a 2D keypoint estimation module for
stereo inputs to improve 3D human pose estimation. The extensive experiments
show that our approach outperforms the previous state-of-the-art methods
qualitatively and quantitatively. UnrealEgo and our source codes are available
on our project web page.Comment: 21 pages, 10 figures, 10 tables; project page:
https://4dqv.mpi-inf.mpg.de/UnrealEgo
SLAM for Visually Impaired People: A Survey
In recent decades, several assistive technologies for visually impaired and
blind (VIB) people have been developed to improve their ability to navigate
independently and safely. At the same time, simultaneous localization and
mapping (SLAM) techniques have become sufficiently robust and efficient to be
adopted in the development of assistive technologies. In this paper, we first
report the results of an anonymous survey conducted with VIB people to
understand their experience and needs; we focus on digital assistive
technologies that help them with indoor and outdoor navigation. Then, we
present a literature review of assistive technologies based on SLAM. We discuss
proposed approaches and indicate their pros and cons. We conclude by presenting
future opportunities and challenges in this domain.Comment: 26 pages, 5 tables, 3 figure
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