# Human Motion Capture Using a Drone

arXiv: https://arxiv.org/abs/1804.06112 · HTML: https://mlsanity.com/paper/1804.06112/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 7 of 7)

- [Proactive Multi-Camera Collaboration For 3D Human Pose Estimation](https://mlsanity.com/paper/2303.03767/) (2023)
- [AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2201.08093/) (2022)
- [Dyadic Human Motion Prediction](https://mlsanity.com/paper/2112.00396/) (2021)
- [3D Human Reconstruction in the Wild with Collaborative Aerial Cameras](https://mlsanity.com/paper/2108.03936/) (2021)
- [Skeleton-Graph: Long-Term 3D Motion Prediction From 2D Observations Using Deep Spatio-Temporal Graph CNNs](https://mlsanity.com/paper/2109.10257/) (2021)
- [Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones](https://mlsanity.com/paper/2307.01559/) (2023)
- [Real-Time Hybrid Mapping of Populated Indoor Scenes using a Low-Cost Monocular UAV](https://mlsanity.com/paper/2203.02453/) (2022)

## Abstract (arXiv)

Current motion capture (MoCap) systems generally require markers and multiple calibrated cameras, which can be used only in constrained environments. In this work we introduce a drone-based system for 3D human MoCap. The system only needs an autonomously flying drone with an on-board RGB camera and is usable in various indoor and outdoor environments. A reconstruction algorithm is developed to recover full-body motion from the video recorded by the drone. We argue that, besides the capability of tracking a moving subject, a flying drone also provides fast varying viewpoints, which is beneficial for motion reconstruction. We evaluate the accuracy of the proposed system using our new DroCap dataset and also demonstrate its applicability for MoCap in the wild using a consumer drone.