论文标题
在连续手势序列中用于孤立手势分离的非解剖图结构
A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences
论文作者
论文摘要
在过去的几十年中，研究人员对连续的手势识别（CHGR）进行了广泛的研究。最近，已经提出了一种模型来应对连续的手势视频中孤立手势的边界检测的挑战[17]。为了增强模型性能，还可以在[17]中提出的模型中替换手工制作的特征提取器，我们提出了GCN模型，并将其与堆叠的BI-LSTM和注意力模块结合使用，以在视频流中推动时间信息。考虑到骨架模式的GCN模型的突破，我们提出了一种两层GCN模型，以增强3D手骨架功能。最后，从[17]借用的每个隔离手势的类概率被喂入后处理模块。此外，我们用一些非解剖图结构代替了解剖图结构。由于缺乏大型数据集，包括连续手势序列和相应的孤立手势，三个动态手势识别（DHGR）中的公共数据集，RKS-Persiansign和Aslvid用于评估。实验结果表明，所提出的模型在处理连续手势序列中检测孤立的手势边界方面的优越性
Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences
