收稿日期: 2014-11-20
修回日期: 2015-06-12
网络出版日期: 2016-03-15
基金资助
Supported by National Natural Science Foundation of China (61431017)
DBN model-based golf swing reconstruction using Kinect data
Received date: 2014-11-20
Revised date: 2015-06-12
Online published: 2016-03-15
Supported by
Supported by National Natural Science Foundation of China (61431017)
人们对高尔夫运动的兴趣在过去的10年间爆发式增长,同时高尔夫球员的数量也显著增加.因此,如何训练一个球员做出适当和准确的挥杆动作已经引起研究者的广泛关注.在此类研究中最重要的步骤是挥杆动作的捕获与重建.至今为止,以Kinect为代表的深度成像设备受其基础条件的限制,其捕获的挥杆运动可能会由于运动中肢体之间的互相遮挡与肢体识别时产生的混乱丧失足够的精确度.为了从肢体互相遮挡并且分辨率较低的深度图像信息中恢复比较精确的运动信息,该文提出一个用于描述人体关节点之间空间关系与关节点动态特性的动态贝叶斯网络(DBN)模型,并基于该模型实现了高尔夫挥杆动作的重建.实验结果表明,该算法能够实现重建精度媲美商用的光学运动捕获系统(OMocap),并且比现有的深度信息修改算法具有更好的性能.
吕东岳 , 黄志蓓 , 陶冠宏 , 俞能海 , 吴健康 . 基于DBN模型与Kinect数据的高尔夫挥杆重建[J]. 中国科学院大学学报, 2016 , 33(2) : 204 -212 . DOI: 10.7523/j.issn.2095-6134.2016.02.009
The last decade has witnessed an explosion of interest in golf, and the number of golf players has increased significantly. Therefore, how to train a golfer to make a proper and accurate golf swing has attracted extensive research attentions. Among these researches the most important step is swing capture and reconstruction. Thus far, restricted to the development of present depth imaging devices, of which the most famous one is Kinect, the initial captured swing movement may not be acceptable enough due to occlusions and mixing up of body parts. To restore motion information from self-occlusion and reconstruct 3D golf swing from low resolution depth data, a dynamic Bayesian network (DBN) model based golf swing reconstruction algorithm is proposed to increase the capture accuracy which integrates the spatial relationship among joints and their movement dynamics. Experimental results have proved that the proposed algorithm can achieve comparable reconstruction accuracy to the commercial optical motion caption (OMocap) system and better performance than state of art modification algorithms using depth information.
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