<-上一篇/Previous Article 下一篇/Next Article->

[1]赵军,於俊,汪增福.基于改进逆向运动学的人体运动跟踪[J].智能系统学报编辑部,2015,10(4):548-554.[doi:10.3969/j.issn.1673-4785.201403032]
　ZHAO Jun,YU Jun,WANG Zengfu.Human motion tracking based on an improved inverse kinematics[J].CAAI Transactions on Intelligent Systems,2015,10(4):548-554.[doi:10.3969/j.issn.1673-4785.201403032]

点击复制

基于改进逆向运动学的人体运动跟踪

PDF下载 HTML

《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷: 10 期数: 2015年第4期页码: 548-554 栏目: 学术论文—智能系统出版日期: 2015-08-25

Title:: Human motion tracking based on an improved inverse kinematics

作者:: 赵军^1,2, 於俊^1,2, 汪增福^1,2,3; 1. 中国科学技术大学信息学院自动化系, 安徽合肥 230026;
2. 语音及语言信息处理国家工程实验室, 安徽合肥 230026;
3. 中国科学院合肥智能机械研究所, 安徽合肥 230026

Author(s):: ZHAO Jun^1,2, YU Jun^1,2, WANG Zengfu^1,2,3; 1. Department of Automation, University of Science and Technology of China, Hefei 230027, China;
2. National Laboratory of Speech and Language Information Processing, Hefei 230027, China;
3. Institute of Intelligent Machine, Chinese Academy of Sciences, Hefei 230027, China

关键词:: 改进逆向运动学; 基于视觉人体运动跟踪; 模板匹配; 计算机视觉; 用户接口

Keywords:: improved inverse kinematics; human motion tracking; template matching; computer vision; user interface

分类号:: TH186

DOI:: 10.3969/j.issn.1673-4785.201403032

文献标志码:: A

摘要:: 随着人们对智能系统需求逐年增高,基于视觉的运动研究引起计算机视觉领域工作者更多的关注。这使它成为模式识别、行为学、行为处理分析与处理等学科的研究热门。现存算法存在需要标记、相机标定等各种约束条件,不能满足人们对人体运行跟踪的需求。论述了一种结合改进逆向运动学和图像模板匹配算法的人体运动位置的跟踪方法。该算法以改进逆向运动学为框架,首先依据逆向运动学知识与正向运动学知识计算出的关节点的粗略位置,对外观模型的各个模块进行模板匹配,接着确定关节点的最优位置,然后确定关节点的三维坐标值,最后重构得到三维动作序列。实验表明,在主观视觉感受与客观衡量标准两方面,此算法获得的实验结果都能够逼近乃至达到人体运动跟踪领域的最佳水准。

Abstract:: With the rising demand for intelligent systems, the study of vision-based human motion is drawing the machine vision investigators, making it become the research focus of pattern recognition, behavioral science, behavior analyzing and processing. The existing algorithms have many kinds of restriction conditions, such as marking and camera calibration, not being able to meet people’s demand for tracking human motion. Therefore, this article proposes a human motion position tracking algorithm on the basis of video, combining the template matching and improved inverse kinematics. It first calculates the coarse position of joint point according to the inverse kinematics and forward kinematics, then applies template matching to each module of the appearing-model, and then determines the optimal location of joints and the 3D coordinates of joints, and finally obtains the 3D action sequences by reconstruction. Experimental results show that this algorithm can be close to and even reach the best level in both subjective visual feel and objective weighing standard in the field of human motion tracking.

参考文献/References:: [1] 阮涛涛, 姚明海, 瞿心昱, 等. 基于视觉的人体运动分析综述[J]. 计算机系统应用, 2010, 20(2): 245-253. RUAN Taotao, YAO Minghai, QU Xinyu, et al. A survey of vision-based human motion analysis[J]. Computer Systems & Applications, 2010, 20(2): 245-253.
[2] 李豪杰, 林守勋, 张勇东. 基于视频的人体运动捕捉综述[J]. 计算机辅助设计与图形学学报, 2006, 18(11): 1645-1650. LI Haojie, LIN Shouxun, ZHANG Yongdong. A survey of video based human motion capture[J]. Journal of Computer-Aided Design & Computer Graphics, 2006, 18(11): 1645-1650.
[3] 龚文凌, 王洪澄, 孙敏. 视频交通监控系统中运动车辆捕捉算法的研究[J]. 微型机与应用, 2004, 23(3): 45-46. GONG Wenling, WANG Hongcheng, SUN Min. The research of motorial vehicle capture arithmetic on vision-based traffic monitor system [J]. Microcomputer & Its Applications, 2004, 23(3): 45-46.
[4] 万成凯. 无标记人体运动捕捉及姿态估计的研究[D]. 北京: 北京交通大学, 2009: 2-10. WAN Chengkai. Research on marker-less human body motion capture and pose estimation [D]. Beijing: Beijing Jiaotong University, 2009: 2-10.
[5] MULTON F, KULPA R, HOYET L, et al. Interactive animation of virtual humans based on motion capture data[J]. Computer Animation and Virtual Worlds, 2009, 20(5/6): 491-500.
[6] 伍星. 基于第二代Bandelet变换的人体检测方法研究[D]. 西安: 西安电子科技大学, 2010: 5-10. WU Xing. Research on method in human detection based on second generation Bandelet transform [D]. Xi’an: Xidian University, 2010: 5-10.
[7] GAVRILA D M. The visual analysis of human movement: a survey[J]. Computer Vision and Image Understanding, 1999, 73(1): 82-98.
[8] JIANG Zhuolin, LIN Zhe, DAVIS L S. Recognizing human actions by learning and matching shape-motion prototype trees[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 533-547.
[9] ELGAMMAL A, SHET V, YACOOB Y, et al. Learning dynamics for exemplar-based gesture recognition[C] //2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, WI, USA: IEEE, 2003, 1:571-578.
[10] AN K H, CHUNG M J. 3D head tracking and pose-robust 2D texture map-based face recognition using a simple ellipsoid model[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008. Nice: IEEE, 2008: 307-312.
[11] MORI G, MALIK J. Recovering 3D human body configurations using shape contexts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(7): 1052-1062.
[12] 苟靖翔. 基于视觉信息特征和机器学习的人体运动跟踪与三维姿势恢复[D]. 西安: 西安电子科技大学, 2012: 10-12. GOU Jingxiang. Human pose estimation and 3D recovery based on visual geometric features and machine learning[D]. Xi’an: Xidian University, 2012: 10-12.
[13] 刘琼, 彭光正, 刘昊. 基于粒子滤波算法的三维关节型人体运动跟踪[J]. 北京理工大学学报, 2011, 31(2): 163-167. LIU Qiong, PENG Guangzheng, LIU Hao. 3D articulated human body tracking by particle filter[J]. Journal of Beijing Institute of Technology, 2011, 31(2): 163-167.
[14] 邹北骥, 陈姝. 适用于单目视频的无标记三维人体运动跟踪[J]. 计算机辅助设计与图形学学报, 2008, 20(8): 1047-1055. ZOU Beiji, CHEN Shu. Markerless 3D human motion tracking for monocular video sequences[J]. Journal of Computer-Aided Design & Computer Graphics, 2008, 20(8): 1047-1055.
[15] 蔡杰, 郑江滨. 双目视觉下三维人体运动跟踪算法[J]. 计算机应用研究, 2009, 26(4): 1279-1281.CAI Jie, ZHENG Jiangbin. 3D human motion tracking algorithm in binocular camera system[J]. Application Research of Computers, 2009, 26(4): 1279-1281.
[16] 陶霖密, 于亚鹏. 摄像机几何约束及人体定位[J]. 中国图象图形学报, 2012, 17(9): 1150-1157. TAO Linmi, YU Yapeng. Geometric constraints of locating cameras and people[J]. Journal of Image and Graphics, 2012, 17(9): 1150-1157.
[17] 陈坚, 吴恩华. 单目视频中人体三维运动的迭代优化估计[J]. 计算机辅助设计与图形学学报, 2005, 17(7): 1523-1528. CHEN Jian, WU Enhua. 3D human motion reconstruction from monocular videos through iterative optimization[J]. Journal of Computer-Aided Design & Computer Graphics, 2005, 17(7): 1523-1528.
[18] 马颂德, 张正友. 计算机视觉——计算机理论与算法基础[M]. 北京: 科学出版社, 1998: 52.

备注/Memo

收稿日期:2014-03-12;改回日期:。
基金项目:国家自然科学基金资助项目(61303150).
通讯作者:赵军.E-mail:zhuyihang1123@hotmail.com.

更新日期/Last Update: 2015-08-28

基于改进逆向运动学的人体运动跟踪 PDF下载HTML

备注/Memo

基于改进逆向运动学的人体运动跟踪

PDF下载 HTML