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Non-verbal communication is often 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 real-time recognition of robot to robot hand signals exchanged during a soccer game. A library for estimating body, face, hands, and foot position has been trained using simulated data and tested on real images. Quantitative experiments carried out on a NAO V6 robot demonstrate the effectiveness of the proposed approach. Source code and data used in this work are made publicly available for the community.
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