论文标题
美国手语的行动认可
Action Recognition for American Sign Language
论文作者
论文摘要
在这项研究中，我们提出了从一系列手势中识别美国手语的发现。尽管大多数文献研究仅着眼于静态手印，但我们的工作目标动态手势。由于动态符号数据集很少，因此我们收集了150个视频的初始数据集，用于10个符号，扩展225个视频，用于15个标志。我们将转移学习模型与深度神经网络和背景减法结合使用，以在不同的时间环境中进行视频。我们的主要结果表明，使用Densenet201，LSTM，带有12帧的视频序列，我们可以获得$ 0.86 $和0.71美元的准确度。
In this research, we present our findings to recognize American Sign Language from series of hand gestures. While most researches in literature focus only on static handshapes, our work target dynamic hand gestures. Since dynamic signs dataset are very few, we collect an initial dataset of 150 videos for 10 signs and an extension of 225 videos for 15 signs. We apply transfer learning models in combination with deep neural networks and background subtraction for videos in different temporal settings. Our primarily results show that we can get an accuracy of $0.86$ and $0.71$ using DenseNet201, LSTM with video sequence of 12 frames accordingly.
