Header logo is

Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles

2019

Conference Paper

ps


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.

Author(s): Nitin Saini and Eric Price and Rahul Tallamraju and Raffi Enficiaud and Roman Ludwig and Igor Martinović and Aamir Ahmad and Michael Black
Book Title: International Conference on Computer Vision
Year: 2019
Month: October

Department(s): Perceiving Systems
Research Project(s): AirCap: 3D Motion Capture
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Place: Seoul, South Korea

State: Accepted

BibTex

@inproceedings{Nitin_ICCV_19,
  title = {Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles},
  author = {Saini, Nitin and Price, Eric and Tallamraju, Rahul and Enficiaud, Raffi and Ludwig, Roman and Martinović, Igor and Ahmad, Aamir and Black, Michael},
  booktitle = {International Conference on Computer Vision},
  month = oct,
  year = {2019},
  month_numeric = {10}
}