Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/78126
Título: | Contactless human-computer interaction using a Deep Neural Network Pipeline for real-time video interpretation and classification |
Autor(es): | Sousa, Regina Jesus, Tiago Rafael Andrade Alves, Victor Machado, José Manuel |
Palavras-chave: | Computer vision Deep neural networks Desktop task simulator Hand gesture recognition Human-computer interaction |
Data: | 2021 |
Editora: | Springer, Cham |
Revista: | Communications in Computer and Information Science |
Citação: | Sousa, R., Jesus, T., Alves, V., Machado, J. (2021). Contactless Human-Computer Interaction Using a Deep Neural Network Pipeline for Real-Time Video Interpretation and Classification. In: Guarda, T., Portela, F., Santos, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2021. Communications in Computer and Information Science, vol 1485. Springer, Cham. https://doi.org/10.1007/978-3-030-90241-4_17 |
Resumo(s): | Nowadays, all applications are developed with the user’s comfort in mind. Regardless of the application’s objective, it should be as simple as possible so that it is easily accepted by its users. With the evolution of technology, simplicity has evolved and has become intrinsically related to the automation of tasks. Therefore, many researchers have focused their investigations on the interaction between humans and computing devices. However, this interaction is usually still carried out via a keyboard and/or a mouse. We present an essemble of deep neural networks for the detection and interpretation of gestural movement, in various environments. Its purpose is to introduce a new form of interaction between the human and computing devices in order to evolve this paradigm. The use case focused on detecting the movement of the user’s hands in real time and automatically interpreting the movement. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/78126 |
ISBN: | 978-3-030-90240-7 |
e-ISBN: | 978-3-030-90241-4 |
DOI: | 10.1007/978-3-030-90241-4_17 |
ISSN: | 1865-0929 |
Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-90241-4_17 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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artigo_artiis.pdf Acesso restrito! | 14,63 MB | Adobe PDF | Ver/Abrir |