Abstract
Natural communication between humans involves hand gestures, which has an impact on research in human-robot interaction. In a real-world scenario, understanding human gestures by a robot is hard due to several challenges like hand segmentation. To recognize hand postures this paper proposes a novel convolutional implementation. The model is able to recognize hand postures recorded by a robot camera in real-time, in a real-world application scenario. The proposed model was also evaluated with a benchmark database and showed better results than the ones reported in the benchmark paper.
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Barros, P., Magg, S., Weber, C., Wermter, S. (2014). A Multichannel Convolutional Neural Network for Hand Posture Recognition. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_51
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DOI: https://doi.org/10.1007/978-3-319-11179-7_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
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