Summary of New Keypoint-based Approach For Recognising British Sign Language (bsl) From Sequences, by Oishi Deb and Kr Prajwal and Andrew Zisserman
New keypoint-based approach for recognising British Sign Language (BSL) from sequences
by Oishi Deb, KR Prajwal, Andrew Zisserman
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed keypoint-based classification model outperforms its RGB-based counterpart in terms of computational efficiency and memory usage when recognizing British Sign Language (BSL) words within continuous signing sequences. The BOBSL dataset is used to assess the model’s performance, showcasing faster training times and reduced resource demands. This novel approach marks the first application of a keypoint-based model for BSL word classification, precluding direct comparisons with existing works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to recognize British Sign Language (BSL) words in everyday signing sequences. Instead of using images like regular computers do, this method uses special points called keypoints to identify signs. This approach is faster and uses less computer power than the old way. The researchers tested their model on a big dataset and found it worked really well. |
Keywords
» Artificial intelligence » Classification
