论文标题
十二指肠：通过双重深度传感器识别静态手势
Duodepth: Static Gesture Recognition Via Dual Depth Sensors
论文作者
论文摘要
静态手势识别是用户及其设备之间的有效的非语言通信渠道。但是，许多现代方法对用户手相对相对于捕获设备的相对姿势敏感，因为手势的一部分可能会被阻塞。我们通过同步记录从两个深度摄像机进行同步记录来提供两种方法，以减轻这种遮挡问题。一种是使用迭代最接近的点注册来准确融合点云和单个点网架构进行分类的更经典方法，而另一种是无需注册而进行分类的双点网络体系结构。在手动收集的20,100点云的数据集中，与标准的单个摄像机管道相比，融合点云方法的错误分类降低了39.2％，双点网的错误分类为53.4％。
Static gesture recognition is an effective non-verbal communication channel between a user and their devices; however many modern methods are sensitive to the relative pose of the user's hands with respect to the capture device, as parts of the gesture can become occluded. We present two methodologies for gesture recognition via synchronized recording from two depth cameras to alleviate this occlusion problem. One is a more classic approach using iterative closest point registration to accurately fuse point clouds and a single PointNet architecture for classification, and the other is a dual Point-Net architecture for classification without registration. On a manually collected data-set of 20,100 point clouds we show a 39.2% reduction in misclassification for the fused point cloud method, and 53.4% for the dual PointNet, when compared to a standard single camera pipeline.
