扩充至100万张图像，提升手势识别精度与泛化能力
HaGRIDv2: 1M Images for Static and Dynamic Hand Gesture Recognition
- 新增15种动态手势，含双手操作和自然手部动作
- 误报率降低6倍，预训练模型性能超越原版
- 适合手势交互、人机协作系统开发者使用
本文提出广泛使用的手势识别数据集HaGRID的第二个版本——HaGRIDv2。新增15种包含对话与控制功能的手势，涵盖双手操作。在原有框架基础上，改进动态手势识别算法，并增加三类新操作手势。通过引入自然手部动作样本，丰富“无手势”类别，使误报率降低6倍。结合原版数据，新版本在手势相关任务的预训练模型上表现更优。同时，其在手势与手部检测数据集中展现出最佳泛化能力。此外，扩散模型生成的手势质量也得到提升。HaGRIDv2、预训练模型及动态手势识别算法均已公开。
原文摘要 · Abstract (English)
This paper proposes the second version of the widespread Hand Gesture Recognition dataset HaGRID -- HaGRIDv2. We cover 15 new gestures with conversation and control functions, including two-handed ones. Building on the foundational concepts proposed by HaGRID's authors, we implemented the dynamic gesture recognition algorithm and further enhanced it by adding three new groups of manipulation gestures. The ``no gesture" class was diversified by adding samples of natural hand movements, which allowed us to minimize false positives by 6 times. Combining extra samples with HaGRID, the received version outperforms the original in pre-training models for gesture-related tasks. Besides, we achieved the best generalization ability among gesture and hand detection datasets. In addition, the second version enhances the quality of the gestures generated by the diffusion model. HaGRIDv2, pre-trained models, and a dynamic gesture recognition algorithm are publicly available.
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