Recognition of Human Actions Through Deep Neural Networks for Multimedia Systems Interaction

Nowadays, interactive multimedia systems are part of everyday life. The most common way to interact and control these devices is through remote controls or some sort of touch panel. In recent years, due to the introduction of reliable low-cost Kinect-like sensing technology, more and more attention has been dedicated to touchless interfaces. A Kinect-like devices can be positioned on top of a multimedia system, detect a person in front of the system and process skeletal data, optionally with RGBd data, to determine user gestures. The gestures of the person can then be used to control, for example, a media device. Even though there is a lot of interest in this area, currently, no consumer system is using this type of interaction probably due to the inherent difficulties in processing raw data coming from Kinect cameras to detect the user intentions. In this work, we considered the use of neural networks using as input only the Kinect skeletal data for the task of user intention classification. We compared different deep networks and analyzed their outputs.