面向多用户行为的无线识别关键技术研究

项目名称： 面向多用户行为的无线识别关键技术研究

项目编号： No.61572512

项目类型： 面上项目

立项/批准年度： 2016

项目学科： 自动化技术、计算机技术

项目作者： 吕绍和

作者单位： 中国人民解放军国防科学技术大学

项目金额： 16万元

中文摘要： 无线通信技术的发展与应用使得无线信号无处不在。除了用于数据通信，无线信号在传播过程中的变化也提供了环境感知的能力。对人类行为的获取与分析是实现信息世界、物理世界的关联与融合的重要手段，在人机交互、增强现实等应用前景广阔。现有工作多聚焦于动作的识别，对行为即动作序列及并发行为的研究甚少。本课题研究利用窄带无线信号实现多用户行为识别的关键技术，针对部分动作难以检测的问题，建立动作关联图模型描述动作的可区分性，探索三维空间的优化部署策略，研究基于机器学习的信号特征提取方法。研究成果将提升对人类行为的感知能力，拓宽无线技术的应用领域，具有重要的理论与实用价值。

中文关键词： 行为感知；深度学习；窄带无线信号；驾驶行为识别；手势识别

英文摘要： With the development and application of wireless communication technology, the wireless signals become ubiquitous. In addition to data communication, the changes of the wireless signals during the signal propagation also provide the ability of environmental perception. In particular, acquisition and analysis of human behaviors is a very important means in the association and integration of the information world and the physical world, which can be adopted widely in human-computer interaction, augmented reality and other prospects. Most of existing work however focus only on the action recognition, the study of activity, i.e., a sequence of action, and the detection of concurrent activities from multiple persons is poorly understood. This research strives to take advantage of the narrowband wireless signal to recognize the multi-person activities. To address the challenge that part of actions are easily confused, we establish a novel action correlation graph model to characterize whether or not two actions can be distinguished, explore the deployment policy in a 3D space to optimize the action recognition performance, and develop efficient machine learning algorithms to extract crucial signal features. The result is expected to enhance the capability of human activity recognition, expand the applications of wirel

英文关键词： action recognition；deep learning；wireless signal；driving action recognition；gesture recognition