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

Marco La Cascia, Filippo Vella, Ignazio Infantino

Risultato della ricerca: Other

Abstract

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.
Lingua originaleEnglish
Pagine71-76
Numero di pagine6
Stato di pubblicazionePublished - 2019

Fingerprint

Multimedia systems
Remote control
Cameras
Neural networks
Processing
Costs
Deep neural networks

Cita questo

Recognition of Human Actions Through Deep Neural Networks for Multimedia Systems Interaction. / La Cascia, Marco; Vella, Filippo; Infantino, Ignazio.

2019. 71-76.

Risultato della ricerca: Other

@conference{8e957b95c32844d8aa80fd8adb87fe0e,
title = "Recognition of Human Actions Through Deep Neural Networks for Multimedia Systems Interaction",
abstract = "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.",
author = "{La Cascia}, Marco and Filippo Vella and Ignazio Infantino",
year = "2019",
language = "English",
pages = "71--76",

}

TY - CONF

T1 - Recognition of Human Actions Through Deep Neural Networks for Multimedia Systems Interaction

AU - La Cascia, Marco

AU - Vella, Filippo

AU - Infantino, Ignazio

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/349579

UR - https://www.thinkmind.org/index.php?view=instance&instance=MMEDIA+2019

M3 - Other

SP - 71

EP - 76

ER -