Efficient Hand Gesture Recognition for Human-Robot Interaction
In this paper, we present an efficient and reliable deep-learning approach that allows users to communicate with robots via hand gesture recognition. Contrary to other works which use external devices such as gloves [1] or joysticks [2] to tele-operate robots, the proposed approach uses only visual...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/295873 |
| Acesso em linha: | http://hdl.handle.net/10261/295873 |
| Access Level: | acceso abierto |
| Palavra-chave: | Deep learning Gesture recognition Human-robot interaction |
| Resumo: | In this paper, we present an efficient and reliable deep-learning approach that allows users to communicate with robots via hand gesture recognition. Contrary to other works which use external devices such as gloves [1] or joysticks [2] to tele-operate robots, the proposed approach uses only visual information to recognize user's instructions that are encoded in a set of pre-defined hand gestures. Particularly, the method consists of two modules which work sequentially to extract 2D landmarks of hands -ie. joints positions- and to predict the hand gesture based on a temporal representation of them. The approach has been validated in a recent state-of-the-art dataset where it outperformed other methods that use multiple pre-processing steps such as optical flow and semantic segmentation. Our method achieves an accuracy of 87.5% and runs at 10 frames per second. Finally, we conducted real-life experiments with our IVO robot to validate the framework during the interaction process. |
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