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Found 7 papers, 5 papers with code
Airship Formations for Animal Motion Capture and Behavior Analysis
Using UAVs for wildlife observation and motion capture offers manifold advantages for studying animals in the wild, especially grazing herds in open terrain.
Learning from synthetic data generated with GRADE
To solve this, we present a fully customizable framework for generating realistic animated dynamic environments (GRADE) for robotics research, first introduced in [1].
Synthetic Data-based Detection of Zebras in Drone Imagery
Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training.
Accelerated Video Annotation driven by Deep Detector and Tracker
In this paper, we propose a new annotation method which leverages a combination of a learning-based detector (SSD) and a learning-based tracker (RE$^3$).
SmartMocap: Joint Estimation of Human and Camera Motion using Uncalibrated RGB Cameras
Second, we learn a probability distribution of short human motion sequences ($\sim$1sec) relative to the ground plane and leverage it to disambiguate between the camera and human motion.
AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation
In this letter, we present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments that uses a team of autonomous unmanned aerial vehicles (UAVs) with on-board RGB cameras and computation.
AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning
We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose and shape of a single moving person using multiple micro aerial vehicles.
