Grassmannian Representation of Motion Depth for 3D Human Gesture and Action Recognition
By Rim Slama, Hazem Wannous, Mohamed Daoudi. 2014
37 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Fei Han
- Boulbaba Ben Amor
- Jingyong Su
- Ennio Gambi
- Susanna Spinsante
- Enea Cippitelli
- William A. Hoff
- Brian Reily
In Human-Computer Interaction
- William A. Hoff
- Anuj Srivastava
- Brian Reily
- Fei Han
- Ennio Gambi
- Susanna Spinsante
- Enea Cippitelli
- Boulbaba Ben Amor
Citations in the corpus
- Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “[183], [184] Grassmann Manifolds BoW Manif Dict X X X X X”
- A Human Activity Recognition System Using Skeleton Data from RGBD Sensors (2016, background): “Depth images can be seen as sequence features modeled temporally as subspaces lying on the Grassmann manifold [11].”
- Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories (2016, methodology): “Some of these datasets are also used in the experimental studies described later.”