3D human action segmentation and recognition using pose kinetic energy
By Junjie Shan, Srinivas Akella. 2014
93 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- German Ignacio Parisi
- Stefan Wermter
- Cornelius Weber
- Ennio Gambi
- Susanna Spinsante
- Enea Cippitelli
- Samuele Gasparrini
- Vincenzo Lomonaco
In Computer Vision and Pattern Recognition
- German Ignacio Parisi
- Stefan Wermter
- Cornelius Weber
- Ennio Gambi
- Susanna Spinsante
- Enea Cippitelli
- Samuele Gasparrini
- Luiza Mici
In Human-Computer Interaction
- Stefan Wermter
- German Ignacio Parisi
- Cornelius Weber
- Ennio Gambi
- Susanna Spinsante
- Enea Cippitelli
- Samuele Gasparrini
- Luiza Mici
Citations in the corpus
- Self-organizing neural integration of pose-motion features for human action recognition (2015, methodology): “Using the same action benchmark for the evaluation, Shan and Akella (2014) used action templates computed from 3D body poses to train multiple classifiers: Hidden Markov Model, Random Forests, K-Nearest Neighbor, and Support Vector Machine (SVM).”
- A Human Activity Recognition System Using Skeleton Data from RGBD Sensors (2016, methodology): “Some solutions for segmentation have been proposed, but most of them are based on thresholds on movements, which can be highly data-dependent [46].”