Continual Learing of Hand Gestures for Human Robot Interaction
Human communication is multimodal. For years, natural language processing has been studied as a form of human-machine or human-robot interaction. In recent years, computer vision techniques have been applied to the recognition of static and dynamic gestures, and progress is being made in sign langua...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/376650 |
| Acceso en línea: | https://hdl.handle.net/2117/376650 |
| Access Level: | acceso abierto |
| Palabra clave: | Computer vision Human-computer interaction Artificial intelligence Visió per computador aprenentatge continu interacció humà-robot reconeixement de gestos aprenentatge màquina xarxes neuronals intel·ligència artificial Computer Vision Continual Learning Human-Robot Interaction Hand Gesture Recognition Machine Learning Deep Learning Artificial Intelligence Visió per ordinador Interacció persona-ordinador Intel·ligència artificial Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | Human communication is multimodal. For years, natural language processing has been studied as a form of human-machine or human-robot interaction. In recent years, computer vision techniques have been applied to the recognition of static and dynamic gestures, and progress is being made in sign language recognition too. The typical way to train a machine learning algorithm to perform a classification task is to provide training examples for all the classes that need to be identified by the model. In a real-world scenario, such as in the use of assistive robots, it is useful to learn new concepts from interaction. However, unlike biological brains, artificial neural networks suffer from catastrophic forgetting, and as a result, are not good at incrementally learning new classes. In this thesis, the HAnd Gesture Incremental Learning (HAGIL) framework is proposed as a method to incrementally learn to classify static hand gestures. We show that HAGIL is able to incrementally learn up to 36 new symbols using only 5 samples for each old symbol, achieving a final average accuracy of over 90%. In addition to that, the incremental training time is reduced to a 10% of the time required when using all data available. |
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