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.
On Field Gesture-Based Robot-to-Robot Communication with NAO Soccer Players
Bloisi D. D.;
2019-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
