New keypoint-based approach for recognising British Sign Language (BSL) from sequences
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916546465497088 |
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| author | Deb, Oishi Prajwal, KR Zisserman, Andrew |
| author_facet | Deb, Oishi Prajwal, KR Zisserman, Andrew |
| contents | In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assessed using the BOBSL dataset, revealing that the keypoint-based approach surpasses its RGB-based counterpart in computational efficiency and memory usage. Furthermore, it offers expedited training times and demands fewer computational resources. To the best of our knowledge, this is the inaugural application of a keypoint-based model for BSL word classification, rendering direct comparisons with existing works unavailable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09475 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | New keypoint-based approach for recognising British Sign Language (BSL) from sequences Deb, Oishi Prajwal, KR Zisserman, Andrew Computer Vision and Pattern Recognition Artificial Intelligence In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assessed using the BOBSL dataset, revealing that the keypoint-based approach surpasses its RGB-based counterpart in computational efficiency and memory usage. Furthermore, it offers expedited training times and demands fewer computational resources. To the best of our knowledge, this is the inaugural application of a keypoint-based model for BSL word classification, rendering direct comparisons with existing works unavailable. |
| title | New keypoint-based approach for recognising British Sign Language (BSL) from sequences |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.09475 |