.png){kind=icon}
Return
Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2017.2708506.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The Kinect sensing devices have been widely used in current Human-Computer Interaction entertainment. A fundamental issue involved is to detect users' motions accurately and quickly. In this paper, we tackle it by proposing a linear algorithm, which is augmented by feature interaction. The linear property guarantees its speed whereas feature interaction captures the higher order effect from the data to enhance its accuracy. The Schatten-p norm is leveraged to integrate the main linear effect and the higher order nonlinear effect by mining the correlation between them. The resulted classification model is a desirable combination of speed and accuracy. We propose a novel solution to solve our objective function. Experiments are performed on three public Kinect-based entertainment data sets related to fitness and gaming. The results show that our method has its advantage for motion detection in a real-time Kinect entertaining environment.
Keywords:
Feature interaction augmented sparse learning
fast kinect motion detection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
View more organizations
Cited Papers
ShareSaveCombining unsupervised learning and discrimination for 3D action recognition.png)
SIGNAL PROCESSINGOAAI
SIGNAL PROCESSINGIF3.6
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave View more