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Roman Ludwig

dblp:49/5118 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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 · 75% Face, body and person analysis · 25%

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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
3d human pose estimation
0.412019
Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles · ICCV 2019
Computer vision › 3D vision › motion capture
human motion capture
0.412019
Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles · ICCV 2019
Computer vision › Face, body and person analysis
human pose estimation
0.412019
Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles · ICCV 2019
Computer vision › 3D vision › motion capture › human motion capture
markerless motion capture
0.412019
Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles · ICCV 2019

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

pose priors · 0.4optimization · 0.43d human body model · 0.42d joint detection · 0.4
YearPublicationVenuePosition
2019 Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles
abstract
Capturing human motion in natural scenarios means moving motion capture out of the lab and into the wild. Typical approaches rely on fixed, calibrated, cameras and reflective markers on the body, significantly limiting the motions that can be captured. To make motion capture truly unconstrained, we describe the first fully autonomous outdoor capture system based on flying vehicles. We use multiple micro-aerial-vehicles(MAVs), each equipped with a monocular RGB camera, an IMU, and a GPS receiver module. These detect the person, optimize their position, and localize themselves approximately. We then develop a markerless motion capture method that is suitable for this challenging scenario with a distant subject, viewed from above, with approximately calibrated and moving cameras. We combine multiple state-of-the-art 2D joint detectors with a 3D human body model and a powerful prior on human pose. We jointly optimize for 3D body pose and camera pose to robustly fit the 2D measurements. To our knowledge, this is the first successful demonstration of outdoor, full-body, markerless motion capture from autonomous flying vehicles.
Nitin Saini, Eric Price 0002, Rahul Tallamraju, Raffi Enficiaud, Roman Ludwig, Igor Martinovic, Aamir Ahmad, Michael J. Black
ICCV5