On Field Gesture-Based Robot-to-Robot Communication with NAO Soccer Players

Valerio Di Giambattista; Mulham Fawakherji; Vincenzo Suriani; Domenico D. Bloisi; Daniele Nardi

doi:10.1007/978-3-030-35699-6_28

On Field Gesture-Based Robot-to-Robot Communication with NAO Soccer Players

Valerio Di Giambattista
, Mulham Fawakherji
, Vincenzo Suriani
, Domenico D. Bloisi
, Daniele Nardi

Computer Systems Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Gesture-based communication is commonly used by soccer players during matches to exchange information with teammates. Among the possible forms of gesture-based interaction, hand signals are the most used. In this paper, we present a deep learning method for recognizing robot-to-robot hand signals exchanged during a soccer game. A neural network for estimating human body, face, hands, and foot position has been adapted for the application in the robot soccer scenario. Quantitative experiments carried out on NAO V6 robots demonstrate the effectiveness of the proposed approach. Source code and data used in this work are made publicly available for the community.

Original language	English
Title of host publication	Unknown book
Publisher	Springer
DOIs	https://doi.org/10.1007/978-3-030-35699-6_28
State	Published - 2019

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10.1007/978-3-030-35699-6_28

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title = "On Field Gesture-Based Robot-to-Robot Communication with NAO Soccer Players",

abstract = "Gesture-based communication is commonly used by soccer players during matches to exchange information with teammates. Among the possible forms of gesture-based interaction, hand signals are the most used. In this paper, we present a deep learning method for recognizing robot-to-robot hand signals exchanged during a soccer game. A neural network for estimating human body, face, hands, and foot position has been adapted for the application in the robot soccer scenario. Quantitative experiments carried out on NAO V6 robots demonstrate the effectiveness of the proposed approach. Source code and data used in this work are made publicly available for the community.",

author = "\{Di Giambattista\}, Valerio and Mulham Fawakherji and Vincenzo Suriani and Bloisi, \{Domenico D.\} and Daniele Nardi",

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AU - Di Giambattista, Valerio

AU - Fawakherji, Mulham

AU - Suriani, Vincenzo

AU - Bloisi, Domenico D.

AU - Nardi, Daniele

PY - 2019

Y1 - 2019

N2 - Gesture-based communication is commonly used by soccer players during matches to exchange information with teammates. Among the possible forms of gesture-based interaction, hand signals are the most used. In this paper, we present a deep learning method for recognizing robot-to-robot hand signals exchanged during a soccer game. A neural network for estimating human body, face, hands, and foot position has been adapted for the application in the robot soccer scenario. Quantitative experiments carried out on NAO V6 robots demonstrate the effectiveness of the proposed approach. Source code and data used in this work are made publicly available for the community.

AB - Gesture-based communication is commonly used by soccer players during matches to exchange information with teammates. Among the possible forms of gesture-based interaction, hand signals are the most used. In this paper, we present a deep learning method for recognizing robot-to-robot hand signals exchanged during a soccer game. A neural network for estimating human body, face, hands, and foot position has been adapted for the application in the robot soccer scenario. Quantitative experiments carried out on NAO V6 robots demonstrate the effectiveness of the proposed approach. Source code and data used in this work are made publicly available for the community.

UR - https://dx.doi.org/10.1007/978-3-030-35699-6_28

U2 - 10.1007/978-3-030-35699-6_28

DO - 10.1007/978-3-030-35699-6_28

M3 - Conference contribution

BT - Unknown book

PB - Springer

ER -

On Field Gesture-Based Robot-to-Robot Communication with NAO Soccer Players

Abstract

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