Kim Hansol, Kim Yoonkyung, Lee Eui Chul
Department of Computer Science, Sangmyung University, Seoul 110-743, Republic of Korea.
ScientificWorldJournal. 2014;2014:683045. doi: 10.1155/2014/683045. Epub 2014 Sep 1.
Although many three-dimensional pointing gesture recognition methods have been proposed, the problem of self-occlusion has not been considered. Furthermore, because almost all pointing gesture recognition methods use a wide-angle camera, additional sensors or cameras are required to concurrently perform finger gesture recognition. In this paper, we propose a method for performing both pointing gesture and finger gesture recognition for large display environments, using a single Kinect device and a skeleton tracking model. By considering self-occlusion, a compensation technique can be performed on the user's detected shoulder position when a hand occludes the shoulder. In addition, we propose a technique to facilitate finger counting gesture recognition, based on the depth image of the hand position. In this technique, the depth image is extracted from the end of the pointing vector. By using exception handling for self-occlusions, experimental results indicate that the pointing accuracy of a specific reference position was significantly improved. The average root mean square error was approximately 13 pixels for a 1920 × 1080 pixels screen resolution. Moreover, the finger counting gesture recognition accuracy was 98.3%.
尽管已经提出了许多三维指向手势识别方法，但自遮挡问题尚未得到考虑。此外，由于几乎所有的指向手势识别方法都使用广角摄像头，因此需要额外的传感器或摄像头来同时进行手指手势识别。在本文中，我们提出了一种使用单个Kinect设备和骨架跟踪模型在大型显示环境中同时进行指向手势和手指手势识别的方法。通过考虑自遮挡，当手遮挡肩膀时，可以对检测到的用户肩膀位置执行补偿技术。此外，我们基于手部位置的深度图像提出了一种促进手指计数手势识别的技术。在该技术中，深度图像从指向向量的末端提取。通过对自遮挡进行异常处理，实验结果表明特定参考位置的指向精度得到了显著提高。对于1920×1080像素的屏幕分辨率，平均均方根误差约为13像素。此外，手指计数手势识别准确率为98.3%。
