基於Kinect感測裝置之雙手手勢辨識

Two-Hand Gesture Recognition with Kinect Sensor

郭家霖(Chia-Lin Kuo)

指導教授 ： 杜維昌

義守大學/電機資訊學院/資訊工程學系/碩士(2017年)

摘要

隨著人機介面技術的開發，人性化人機互動漸漸成為資訊發展的重要趨勢。不需穿戴任何裝置，可以與機器的溝通更加直覺與自然。由於人們經常使用身體語言來輔助溝通，手勢自然是一個相當重要的溝通方式。為了實現雙手手勢辨識，本研究使用微軟推出的Kinect感測裝置來取得畫面資訊，相較於一般攝影機能夠快速取得深度資訊，受到環境光線的影響較小。首先利用凸包演算法與掌心位置計算手指隻數，得以判別靜態手勢。接著，將一連串靜態手勢對應的字串進行相似度模糊比對，藉以辨識連續雙手的手勢動作，作為人機互動的基礎。

關鍵字

人機互動 ； 手勢辨識 ； 字串比對

並列摘要

With the development of human-machine interface technology, natural human-machine interaction is becoming an important trend of information development. The communication without wearing any device can be more intuitive and user-friendly. Because people often use body language to support communication, gesture is naturally a very important means of communication. In order to achieve the two-hand gesture recognition, this study uses Microsoft's Kinect sensor device to get the visual information. Compared with a general camera, Kinect can quickly get the depth information that is less affected by environmental light. First, the convex hull algorithm is used to calculate the number of fingers with palm position to determine the static gestures. And then, the strings corresponding to a sequence of static gestures are analyzed by using some fuzzy matching algorithms. These strategies of identify the continuous two-hand gesture can be used as the basis of human-machine interaction.

並列關鍵字

human-machine interaction ； gesture recognition ； Kinect ； string matching

參考文獻

[13] 賴彥成，基於深度影像與膚色之即時手勢辨識技術，國立臺北科技大學資訊工程系研究所碩士論文，2013。

[3] H. M. Justina Hsu, “The potential of Kinect as interactive educational technology,” 2nd International Conference on Education and Management Technology, vol. 13, 2011.

[5] Xin Liu, H. R. Xu and Z. Y. Hu, “GPU based fast 3D-object modeling with Kinect,” ACTA Automatica Sinica, vol. 38, no. 8, pp. 1288-1297, 2012.

[8] L. Shi, Y. Wang and J. Li, “A real time vision-based hand gestures recognition system”, Proc. 5th ISICA, pp. 349-358, 2010.

[10] Y. Zhu, G. Xu and D. J. Kriegman, “A real-time approach to the spotting, representation and recognition of hand gestures for human-computer interaction,” Computer Vision and Image Understanding, pp. 189-208, 2002.