论文标题
手语识别的时间累积功能
Temporal Accumulative Features for Sign Language Recognition
论文作者
论文摘要
在本文中，我们提出了一组称为时间累积特征（TAF）的功能，用于表示和识别孤立的手语手势。通过将特定于手语的特定于手语的结构合并以更好地代表手语视频的独特语言特征，我们设计了一种高效且快速的SLR方法来识别孤立的手语手势。提出的方法是基于HSV的累积视频表示，其中基于语言运动模型的密钥帧由不同的颜色表示。我们还结合了手形信息并使用小型卷积神经网络，证明了语言亚基的累积特征的顺序建模在基线分类结果后改善。
In this paper, we propose a set of features called temporal accumulative features (TAF) for representing and recognizing isolated sign language gestures. By incorporating sign language specific constructs to better represent the unique linguistic characteristic of sign language videos, we have devised an efficient and fast SLR method for recognizing isolated sign language gestures. The proposed method is an HSV based accumulative video representation where keyframes based on the linguistic movement-hold model are represented by different colors. We also incorporate hand shape information and using a small scale convolutional neural network, demonstrate that sequential modeling of accumulative features for linguistic subunits improves upon baseline classification results.
