A decision forest based feature selection framework for action recognition from RGB-depth cameras

By Farhood Negin, Firat Özdemir, Kamer Ali Yüksel, C. B. Akgül, A. Erçil. 2013

60 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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

1. Fei Han
2. Hao Zhang
3. Ling Shao
4. Li Liu
5. William A. Hoff
6. Brian Reily
7. J. Laviola
8. Jungong Han

In Computer Vision and Pattern Recognition

1. Fei Han
2. William A. Hoff
3. Brian Reily
4. Li Liu
5. Hao Zhang
6. Jungong Han
7. Ling Shao
8. J. Laviola

Citations in the corpus

• RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added int”
• Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “[206] RDF Kinematic Features Conc Lowlv Unsup X X X Masood et al.”
• MobileRGBD, an open benchmark corpus for mobile RGB-D related algorithms (2014, unknown): “…companion robot, are using RGB-D sensors like the Kinect for many tasks: 3D SLAM, people tracking and skeleton tracking, gesture recognition, body pose estimation, engagement toward a robot or for elderly at home, detecting falls or searching for a fallen person ([2], [3], [4], [5], [6], [7], [8]).”
• 3D Gestural Interaction: The State of the Field (2013, methodology): “[111] Feature selection with decision forest 10 98.”
• The role of RGB-D benchmark datasets: an overview (2013, methodology): “Glocker et al. [9] prvoide a dataset captured with a moving camera and use KinectFusion to generate the 3d scene and the camera path as ground truth for the benchmark.”
• Online robust action recognition based on a hierarchical model (2014, unknown): “[15] introduce a discriminative RDF-based feature selection framework to identify the most effective subset of features localized both in time and space.”