Contour Model-Based Hand-Gesture Recognition Using the Kinect Sensor
Contour Model-Based Hand-Gesture Recognition Using the Kinect Sensor
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DOI：
10.1109/tcsvt.2014.2302538
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发表时间：
2014-01
影响因子：
8.4
通讯作者：
Y. Yao;Y. Fu
中科院分区：
文献类型：
--
作者：
Y. Yao;Y. Fu
在基于RGB-D传感器的姿态估计中，训练数据的收集通常是一项具有挑战性的任务。在本文中，我们提出了一种新的手部动作捕捉方法来建立真实的手势数据集。针对基于颜色的半自动贴标，设计了一种14块手动贴标方案。该方法集成到基于视觉的手势识别框架中，用于开发桌面应用程序。我们使用Kinect传感器在不受约束的条件下实现更可靠和准确的跟踪。此外，提出了一种简化手势匹配过程的手部轮廓模型，降低了手势匹配的计算复杂度。该框架允许在三维空间中跟踪手势，并与简单的轮廓模型匹配手势，从而支持复杂的实时交互。实验评估和手势交互的实际演示证明了该框架的有效性。
In RGB-D sensor-based pose estimation, training data collection is often a challenging task. In this paper, we propose a new hand motion capture procedure for establishing the real gesture data set. A 14-patch hand partition scheme is designed for color-based semiautomatic labeling. This method is integrated into a vision-based hand gesture recognition framework for developing desktop applications. We use the Kinect sensor to achieve more reliable and accurate tracking under unconstrained conditions. Moreover, a hand contour model is proposed to simplify the gesture matching process, which can reduce the computational complexity of gesture matching. This framework allows tracking hand gestures in 3-D space and matching gestures with simple contour model, and thus supports complex real-time interactions. The experimental evaluations and a real-world demo of hand gesture interaction demonstrate the effectiveness of this framework.