摘要本论文利用微软的 Kinect 传感器，结合骨骼和深度图像信息研究了手势识别算法，并完成了一个实时、在线的手势识别系统；使用该手势识别结果控制三维场景变化，完成了一个基于手势识别的三维场景视点控制系统。   在手势识别研究方面，论文分别研究了静态和动态手势识别算法。静态手势识别算法在基于 Kinect 的深度和骨骼坐标初定位的基础上，通过水平及垂直投影，完成手部区域的分割，利用 Hu矩作特征结合 SVM 完成手势识别。动态手势识别通过从深度视频流中获取手的运动轨迹点，并进行等间隔采样，得到手势的运动特征。论文探索了三种动态手势识别算法，并最终确定一种结合运动轨迹模版粗匹配和轨迹切线角 SVM 精确分类的方法。该方法在系统实测中达到了 90%以上的识别率。在此基础上，论文还完成了一个基于手势识别的三维场景视点控制系统，通过将动态手势识别结果融入一个基于 OSG的三维场景漫游器，完成了对三维场景视点变化的控制。 61084  
毕业论文关键词：SVM  Hu 不变矩  静态手势识别  动态手势识别  三维场景控制   
Title  Study on viewpoint control algorithm of 3D scenes   by gesture recognition 
Abstract     Gesture recognition algorithms based on skeleton and depth information captured by Microsoft Kinect are studied in the thesis and a real-time and online gesture recognition system is designed. Furthermore, the gesture recognition system is integrated into a 3D scene viewpoint control system to manipulate scene changes through gesture.     Both static and dynamic gesture recognition methods are discussed in the thesis. As for static gesture recognition, an algorithm consists of three steps is proposed that roughly locates hands based on depth data and skeleton points of Kinect, precisely segments out hands through horizontal and vertical projections and accomplishes gesture recognition using Hu invariant moments and SVM. While the dynamic gesture recognition is to obtain kinetic features via the movement trajectories of hands extracted from the depth data streams and post-processed with uniformly-spaced sub-sampling. Three dynamic gesture recognition algorithms are explored and finally a method combining the template matching of moving trajectories and the SVM classification of the tangent angles of the trajectories is adopted. In practical usage, the proposed method  can achieve a recognition rate over 90%. Finally, a 3D-scene viewpoint control system is designed and implemented that uses the dynamic gesture recognition algorithm to manipulate a 3D scene viewer realized by OSG.   Keywords: SVM, Hu invariant moments, Static gesture recognition,           
 Dynamic gesture recognition，3D scene control

目录

1绪论···1

1.1选题背景和意义··1

1.2手势识别研究现状简介·2

1.3论文完成工作及章节安排··3

2手势识别算法研究·4

2.1Kinect简介4

2.2静态手势识别·5

2.3动态手势识别12

3基于手势识别的三维场景控制系统实现·20

3.1系统结构框图20

3.2静态手势识别的实现···21

3.3动态手势识别的实现···24

3.4三维场景漫游实现··26

3.5Android手势识别实现·29

4系统运行结果··33

4.1实验系统运行结果··33

4.2基于手势识别的三维场景控制系统运行结果35

5总结与展望·36

5.1总结36

5.2展望36

致谢39

参考文献40 Kinect基于手势识别的三维场景视点控制算法研究:http://www.751com.cn/zidonghua/lunwen_66690.html