使用简易深度成像设备的高尔夫挥杆动态贝叶斯网络三维重建

吕东岳; 黄志蓓; 陶冠宏; 俞能海; 吴健康

doi:10.11999/JEIT150165

使用简易深度成像设备的高尔夫挥杆动态贝叶斯网络三维重建

doi: 10.11999/JEIT150165

基金项目:

国家自然科学基金(61431017)和科技部国际科技合作专项(2012DFG11820)

计量
- 文章访问数: 1306
- HTML全文浏览量: 123
- PDF下载量: 667
- 被引次数: 0
出版历程
- 收稿日期: 2015-01-29
- 修回日期: 2015-05-11
- 刊出日期: 2015-09-19

Dynamic Bayesian Network Model Based Golf Swing 3D Reconstruction Using Simple Depth Imaging Device

摘要

摘要: 基于简易深度成像设备的动作捕捉系统因其与传统设备相比更加廉价且易于使用而倍受关注。然而，此类设备图像分辨率很低，肢体间互相遮挡，缺乏3维动作重建的基本数据条件。该文融合人体关节点父子关系与关节点在运动中的多阶马尔可夫性，提出一个描述人体关节点空间关系与动态特性的动态贝叶斯网络(DBN)模型，基于该DBN模型并利用高尔夫挥杆运动的相似性，构建了一种高尔夫挥杆3维重建系统DBN-Motion(DBN-based Motion reconstruction system)，使用简易深度成像设备Kinect，有效地解决了肢体遮挡的问题，实现了高尔夫挥杆动作的捕获和3维重建。实验结果表明，该系统能够在重建精度上媲美商用光学动作捕捉系统。
- 信号处理 /
- 高尔夫挥杆重建 /
- 动态贝叶斯网络模型 /
- 深度成像设备
Abstract: The simple depth imaging device gains more and more attention because of its lower cost and easy- to-use property compared with traditional motion capture systems. However, this kind of devices lack the basic data condition of 3D motion reconstruction due to low resolution, occlusions, and mixing up of body parts. In this paper, a Dynamic Bayesian Network (DBN) model is proposed to describe the spatial and temporal characteristics of human body joints. The model is based on fusion of the parent-child characteristics of joints and multi-order Markov property of joint during motion. A golf swing capture and reconstruction system DBN-Motion (DBN-based Motion reconstruction system), is presented based on the DBN model and the similarity of swing with a simple depth imaging device, Kinect, as capturing device. The proposed system effectively solves the problem of occlusions and mixing up of body parts, and successfully captures and reconstructs golf swing in 3D space. Experimental results prove that the proposed system can achieve comparable reconstruction accuracy to the commercial optical motion caption system.
- Signal processing /
- Golf swing reconstruction /
- Dynamic Bayesian Network (DBN) model /
- Depth imaging device

HTML全文

参考文献(18)

Zhou H and Hu H. Human motion tracking for rehabilitation a survey[J]. Biomedical Signal Processing and Control, 2008, 3(1): 1-18.

Noiumkar S and Tirakoat S. Use of optical motion capture in sports science: a case study of golf swing[C]. 2013 International Conference on Informatics and Creative Multimedia (ICICM), Kuala Lumpur, 2013: 310-313.

Holte M B, Chakraborty B, Gonzalez J, et al.. A local 3-D motion descriptor for multi-view human action recognition from 4-D spatio-temporal interest points[J]. IEEE Journal of Selected Topics in Signal Processing, 2012, 6(5): 553-565.

Nam C N K, Kang H J, and Suh Y S. Golf swing motion tracking using inertial sensors and a stereo camera[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(4): 943-952.

Chun S, Kang D, Choi H R, et al.. A sensor-aided self coaching model for uncocking improvement in golf swing[J]. Multimedia Tools and Applications, 2014, 72(1): 253-279.

Livingston M A, Sebastian J, Ai Z, et al.. Performance measurements for the microsoft kinect skeleton[C]. 2012 IEEE Virtual Reality Short Papers and Posters (VRW), Costa Mesa, CA, 2012: 119-120.

Shum H P, Ho E S, Jiang Y, et al.. Real-time posture reconstruction for Microsoft Kinect[J]. IEEE Transactions on Cybernetics, 2013, 43(5): 1357-1369.

Rosado J, Silva F, Santos V, et al.. Reproduction of human arm movements using kinect-based motion capture data[C]. 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), Shenzhen, 2013: 885-890.

Xiang C, Hsu H H, Hwang W Y, et al.. Comparing real-time human motion capture system using inertial sensors with microsoft kinect[C]. 2014 7th International Conference on Ubi-Media Computing and Workshops (UMEDIA), Ulaanbaatar, 2014: 53-58.

Kao W C, Hsu S C, and Huang C L. Human upper-body motion capturing using kinect[C]. 2014 International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, 2014: 245-250.

Zhang L, Hsieh J C, Ting T T, et al.. A kinect based golf swing score and grade system using GMM and SVM[C]. 2012 5th International Congress on Image and Signal Processing (CISP), Chongqing, 2012: 711-715.

Zhang L, Hsieh J C, and Wang J. A kinect-based golf swing classification system using HMM and Neuro-Fuzzy[C]. 2012 International Conference on Computer Science and Information Processing (CSIP), Xi,an, 2012: 1163-1166.

Lin Y H, Huang S Y, Huang S Y, et al.. A kinect-based system for golf beginners training[J]. Information Technology Convergence, 2013, 253(1): 121-129.

Shen W, Deng K, Bai X, et al.. Exemplar-based human action pose correction and tagging[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, USA, 2012: 1784-1791.

Smisek J, Jancosek M, and Pajdla T. 3D with kinect[C]. 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, 2011: 1154-1160.

Arvind D and Bates A. The speckled golfer[C]. The ICST 3rd International Conference on Body Area Networks, Tempe, Arizona, 2008: 1-7.

McGuan S P. Achieving commercial success with biomechanics simulation[C]. 20 International Symposium on Biomechanics in Sports, Cceres, Spain, 2002: 20, 451-460.

. 8.

施引文献

资源附件(0)

访问统计