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Eran Eilat

dblp:289/1180 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
3D vision · 67% Face, body and person analysis · 33%
Human-computer interaction and pervasive computing
1 paper
Wearable and physiological sensing · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › Face, body and person analysis
human pose estimation
0.112009
Markerless motion capture using a single depth sensor · SIGGRAPH ASIA Sketches 2009
Computer vision › 3D vision › motion capture › human motion capture
markerless motion capture
0.112009
Markerless motion capture using a single depth sensor · SIGGRAPH ASIA Sketches 2009
Computer vision › 3D vision
motion capture
0.112009
Markerless motion capture using a single depth sensor · SIGGRAPH ASIA Sketches 2009
Wearable and physiological sensing › optical sensing
depth sensing
0.012009
Markerless motion capture using a single depth sensor · SIGGRAPH ASIA Sketches 2009

Methods — techniques the papers use, named apart from their topics

multi-person detection · 0.2background noise removal · 0.2
YearPublicationVenuePosition
2009 Markerless motion capture using a single depth sensor
abstract
We present a robust framework for tracking skeleton joints in real-time by using a single time-of-flight depth sensor. The framework is able to remove the background noise inherent in time-of-flight cameras, detect multiple people, and track up to 30 joints of free motion for each person. The approach has several advantages over traditional motion capture, as it is a cheap alternative to magnetic and optical systems, and requires no markers whatsoever. Unlike markerless systems based on RGB cameras [Duetscher et al. 2000; Kehl and Van Gool 2006], our framework yields dependable results at an interactive rate using a single camera.
Amit Bleiweiss, Eran Eilat, Gershom Kutliroff
SIGGRAPH ASIA Sketches2