MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
arXiv:1409.7963
Abstract
In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.
Cited by in corpus (4)
- Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos
- Real-time Human Pose Estimation from Video with Convolutional Neural Networks
- EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
- Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image