选对身体关键点，识别巴西手语更快更准
Proper Body Landmark Subset Enables More Accurate and 5X Faster Recognition of Isolated Signs in LIBRAS
- 筛选合适的关键点子集，替代完整骨架提取
- 识别速度提升5倍以上，准确率不降反升
- 适合资源受限场景的实时手语识别系统
本文研究了轻量级身体关键点检测在识别巴西手语（LIBRAS）孤立手势中的可行性。尽管骨架图像表示显著提升了识别性能，但使用OpenPose进行关键点提取导致处理速度过慢。初步实验发现，仅用轻量级MediaPipe替代OpenPose虽提升了速度，却大幅降低准确率。为此，我们探索了关键点子集选择策略以优化性能。实验结果表明，合理的关键点子集可达到或超过当前最优方法的准确率，同时处理时间减少5倍以上。此外，我们证明基于样条插值的方法能有效缓解关键点缺失问题，带来显著的准确率提升。
原文摘要 · Abstract (English)
This paper examines the feasibility of utilizing lightweight body landmark detection for recognizing isolated signs in Brazilian Sign Language (LIBRAS). Although the use of skeleton-image representation has enabled substantial improvements in recognition performance, the use of OpenPose for landmark extraction hindered time performance. In a preliminary investigation, we observed that simply replacing OpenPose with lightweight MediaPipe, while improving processing speed, significantly reduced accuracy. To overcome this limitation, we explored landmark subset selection strategies to optimize recognition performance. Experimental results show that a proper landmark subset achieves comparable or superior performance to state-of-the-art methods while reducing processing time by more than 5X. As an additional contribution, we demonstrate that spline-based imputation effectively mitigates missing landmark issues, leading to substantial accuracy gains.
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