Deep self-supervised learning with visualisation for automatic gesture recognition
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913396529561600 |
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| author | Allemand, Fabien Mazzela, Alessio Villette, Jun Aspandi, Decky Zaharia, Titus |
| author_facet | Allemand, Fabien Mazzela, Alessio Villette, Jun Aspandi, Decky Zaharia, Titus |
| contents | Gesture is an important mean of non-verbal communication, with visual modality allows human to convey information during interaction, facilitating peoples and human-machine interactions. However, it is considered difficult to automatically recognise gestures. In this work, we explore three different means to recognise hand signs using deep learning: supervised learning based methods, self-supervised methods and visualisation based techniques applied to 3D moving skeleton data. Self-supervised learning used to train fully connected, CNN and LSTM method. Then, reconstruction method is applied to unlabelled data in simulated settings using CNN as a backbone where we use the learnt features to perform the prediction in the remaining labelled data. Lastly, Grad-CAM is applied to discover the focus of the models. Our experiments results show that supervised learning method is capable to recognise gesture accurately, with self-supervised learning increasing the accuracy in simulated settings. Finally, Grad-CAM visualisation shows that indeed the models focus on relevant skeleton joints on the associated gesture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep self-supervised learning with visualisation for automatic gesture recognition Allemand, Fabien Mazzela, Alessio Villette, Jun Aspandi, Decky Zaharia, Titus Computer Vision and Pattern Recognition Human-Computer Interaction Gesture is an important mean of non-verbal communication, with visual modality allows human to convey information during interaction, facilitating peoples and human-machine interactions. However, it is considered difficult to automatically recognise gestures. In this work, we explore three different means to recognise hand signs using deep learning: supervised learning based methods, self-supervised methods and visualisation based techniques applied to 3D moving skeleton data. Self-supervised learning used to train fully connected, CNN and LSTM method. Then, reconstruction method is applied to unlabelled data in simulated settings using CNN as a backbone where we use the learnt features to perform the prediction in the remaining labelled data. Lastly, Grad-CAM is applied to discover the focus of the models. Our experiments results show that supervised learning method is capable to recognise gesture accurately, with self-supervised learning increasing the accuracy in simulated settings. Finally, Grad-CAM visualisation shows that indeed the models focus on relevant skeleton joints on the associated gesture. |
| title | Deep self-supervised learning with visualisation for automatic gesture recognition |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.12440 |