El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In peruvian sign language (PSL), recognition of static gestures has been proposed earlier. However, to state a conversation using sign language, it is also necessary to employ dynamic gestures. We propose a method to extract a feature vector for dynamic gestures of PSL. We collect a dataset with 288 video sequences of words related to dynamic gestures and we state a workflow to process the keypoints of the hands, obtaining a feature vector for each video sequence with the support of a video summarization technique. We employ 9 neural networks to test the method, achieving an average accuracy ranging from 80% and 90%, using 10 fold cross-validation.
| Identifer | oai:union.ndltd.org:PERUUPC/oai:repositorioacademico.upc.edu.pe:10757/656630 |
| Date | 01 September 2020 |
| Creators | Neyra-Gutierrez, Andre, Shiguihara-Juarez, Pedro |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Source Sets | Universidad Peruana de Ciencias Aplicadas (UPC) |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/article |
| Format | application/html |
| Source | Universidad Peruana de Ciencias Aplicadas (UPC), Repositorio Académico - UPC, Proceedings of the 2020 IEEE 27th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2020 |
| Rights | info:eu-repo/semantics/embargoedAccess |
| Relation | https://ieeexplore.ieee.org/abstract/document/9220243 |
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