AirCap Pose Estimator

Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles

Nitin Saini, Eric Price, Rahul Tallamraju, Raffi Enficiaud, Roman Ludwig, Igor Martinović, Aamir Ahmad, Michael Black

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.

Video

Material

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}
}