智能人机交互中第一视角手势表达的一次性学习分类识别

在智能人机交互中,以交互人的视角为第一视角的手势表达发挥着重要作用,而面向第一视角的手势识别则成为最重要的技术环节.本文通过深度卷积神经网络的级联组合,研究复杂应用场景中第一视角下的一次性学习手势识别(One-shot learning hand gesture recognition,OSLHGR)算法.考虑到实际应...

One-shot Learning Classification and Recognition of Gesture Expression From the Egocentric Viewpoint in Intelligent Human-computer Interaction

In intelligent human-computer interaction (HCI), the expression of gestures with the perspective of the interactive person as the egocentric viewpoint plays an important role, while gesture recognition from the egocentric viewpoint becomes the most important technical link. In this paper, one-shot learning hand gesture recognition (OSLHGR) algorithm under the egocentric viewpoint in complex application scenarios is studied through the cascade combination of deep convolutional neural networks (CNN). Considering the convenience and applicability of practical applications, the improved lightweight SSD (single shot multibox detector) detection network was utilized to achieve rapid and accurate gesture object detection. Furthermore, the improved lightweight U-Net network is used as the main tool to perform pixel-level efficient and accurate segmentation of gesture targets in complex backgrounds. On the basis of U-Net results, a networked algorithm for OSLHGR from the egocentric viewpoint is proposed by using the combined 3D deep neural network. A series of experimental results on the Pascal VOC 2012 dataset and the gesture dataset collected by SoftKinetic DS325 show that the proposed networked algorithm has significant advantages in gesture target detection and segmentation precision, classification accuracy and real-time performance. It can provide reliable technical support for the realization of convenient and high-performance intelligent HCI in complex application environment.