In this paper, we present a new approach for dynamic hand gesture recognition. Our goal is to integrate spatiotemporal features extracted from multimodal data captured by the Kinect sensor. In case the skeleton data is not provided, we apply a novel skeleton estimation method to compute temporal features. Furthermore, we introduce an effective method to extract a fixed number of keyframes to reduce the processing time. To extract pose features from RGB-D data, we take advantage of two different approaches: (1) Convolutional Neural Networks and (2) Histogram of Cumulative Magnitudes. We test different integration methods to fuse the extracted spatiotemporal features to boost recognition performance in a linear SVM classifier. Extensive experiments prove the effectiveness and feasibility of the proposed framework for hand gesture recognition.

在本文中，我们提出了一种动态手势识别的新方法。我们的目标是整合从Kinect传感器捕获的多峰数据中提取的时空特征。如果没有提供骨架数据，我们将应用一种新颖的骨架估计方法来计算时间特征。此外，我们引入了一种有效的方法来提取固定数量的关键帧，以减少处理时间。为了从RGB-D数据中提取姿态特征，我们利用了两种不同的方法：（1）卷积神经网络和（2）累积量直方图。我们测试了不同的集成方法，以融合提取的时空特征以提高线性SVM分类器中的识别性能。大量实验证明了所提出的手势识别框架的有效性和可行性。