Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection.

The Kinect sensing devices have been widely used in current HCI 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 datasets related to fitness and gaming. The results show that our method has its advantage for motion detection in a real-time Kinect entertaining environment.