用关键点识别连续手语，更省资源、更快训练。
New keypoint-based approach for recognising British Sign Language (BSL) from sequences
- 基于关键点而非图像像素，提取手势特征
- 在BOBSL数据集上效率更高，内存占用减少
- 首次用于英式手语识别，适合资源受限场景
本文提出一种新型基于关键点的分类模型，用于识别连续手语序列中的英式手语（BSL）词汇。模型在BOBSL数据集上进行评估，结果显示，该方法在计算效率和内存使用方面优于基于RGB图像的方法，训练时间更短，所需计算资源更少。据我们所知，这是首个将关键点模型应用于BSL词汇识别的工作，因此无法与现有方法直接对比。
原文摘要 · Abstract (English)
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.
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