论文标题
使用基于模型的方法和深度色图像识别26个手的自由度
Recognition of 26 Degrees of Freedom of Hands Using Model-based approach and Depth-Color Images
论文作者
论文摘要
在这项研究中，我们提出了一种基于模型的方法，以识别人类手的全部26度自由度。输入数据包括从Kinect摄像头获得的RGB-D图像以及由其解剖结构和图形矩阵构建的手的3D模型。然后定义成本函数，以便在匹配模型和观察图像时实现其最小值。为了解决26维空间中的优化问题，使用了改进的粒子群优化算法。此外，图形处理单元（GPU）中的并行计算用于处理计算昂贵的任务。仿真和实验结果表明，该系统可以识别每帧0.8秒的处理时间的26度自由度。该算法对噪声非常强大，并且使用单个相机简单的硬件需求很简单。
In this study, we present an model-based approach to recognize full 26 degrees of freedom of a human hand. Input data include RGB-D images acquired from a Kinect camera and a 3D model of the hand constructed from its anatomy and graphical matrices. A cost function is then defined so that its minimum value is achieved when the model and observation images are matched. To solve the optimization problem in 26 dimensional space, the particle swarm optimization algorimth with improvements are used. In addition, parallel computation in graphical processing units (GPU) is utilized to handle computationally expensive tasks. Simulation and experimental results show that the system can recognize 26 degrees of freedom of hands with the processing time of 0.8 seconds per frame. The algorithm is robust to noise and the hardware requirement is simple with a single camera.
