This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts som...This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.展开更多
毫米波雷达凭借其出色的环境适应性、高分辨率和隐私保护等优势,在智能家居、智慧养老和安防监控等领域具有广泛的应用前景。毫米波雷达三维点云是一种重要的空间数据表达形式,对于人体行为姿态识别具有极大的价值。然而,由于毫米波雷...毫米波雷达凭借其出色的环境适应性、高分辨率和隐私保护等优势,在智能家居、智慧养老和安防监控等领域具有广泛的应用前景。毫米波雷达三维点云是一种重要的空间数据表达形式,对于人体行为姿态识别具有极大的价值。然而,由于毫米波雷达点云具有强稀疏性,给精准快速识别人体动作带来了巨大的挑战。针对这一问题,该文公开了一个毫米波雷达人体动作三维点云数据集mmWave-3DPCHM-1.0,并提出了相应的数据处理方法和人体动作识别模型。该数据集由TI公司的IWR1443-ISK和Vayyar公司的vBlu射频成像模组分别采集,包括常见的12种人体动作,如走路、挥手、站立和跌倒等。在网络模型方面,该文将边缘卷积(EdgeConv)与Transformer相结合,提出了一种处理长时序三维点云的网络模型,即Point EdgeConv and Transformer(PETer)网络。该网络通过边缘卷积对三维点云逐帧创建局部有向邻域图,以提取单帧点云的空间几何特征,并通过堆叠多个编码器的Transformer模块,提取多帧点云之间的时序关系。实验结果表明,所提出的PETer网络在所构建的TI数据集和Vayyar数据集上的平均识别准确率分别达到98.77%和99.51%,比传统最优的基线网络模型提高了大约5%,且网络规模仅为1.09 M,适于在存储受限的边缘设备上部署。展开更多
文摘This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.
文摘毫米波雷达凭借其出色的环境适应性、高分辨率和隐私保护等优势,在智能家居、智慧养老和安防监控等领域具有广泛的应用前景。毫米波雷达三维点云是一种重要的空间数据表达形式,对于人体行为姿态识别具有极大的价值。然而,由于毫米波雷达点云具有强稀疏性,给精准快速识别人体动作带来了巨大的挑战。针对这一问题,该文公开了一个毫米波雷达人体动作三维点云数据集mmWave-3DPCHM-1.0,并提出了相应的数据处理方法和人体动作识别模型。该数据集由TI公司的IWR1443-ISK和Vayyar公司的vBlu射频成像模组分别采集,包括常见的12种人体动作,如走路、挥手、站立和跌倒等。在网络模型方面,该文将边缘卷积(EdgeConv)与Transformer相结合,提出了一种处理长时序三维点云的网络模型,即Point EdgeConv and Transformer(PETer)网络。该网络通过边缘卷积对三维点云逐帧创建局部有向邻域图,以提取单帧点云的空间几何特征,并通过堆叠多个编码器的Transformer模块,提取多帧点云之间的时序关系。实验结果表明,所提出的PETer网络在所构建的TI数据集和Vayyar数据集上的平均识别准确率分别达到98.77%和99.51%,比传统最优的基线网络模型提高了大约5%,且网络规模仅为1.09 M,适于在存储受限的边缘设备上部署。
