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通过整合深度和颜色信息，深度摄像机Kinect能够稳健的侦测出人体及人体骨架关节点，为计算机视觉、人体行为识别、机器人学的发展带来了革命性的进步. 然而单台深度摄像机的侦测范围有限. 虽然采用多台深度摄像机所构建的摄像机网可有效的扩大侦测范围，但是必须依赖深度摄影机之间的相对位置与朝向的精确标定. 论文采用作者之前提出的以人体骨架为基础的视角无关转换技术，能快速稳健的标定出深度摄像机之间的位置关系. 通过利用相邻两台深度摄影机同时侦测到的人体骨架，论文能直接利用深度摄影机所量测的人体上半身中稳定的关节点为新坐标系的参考点，实时的计算出两摄影机之间的平移向量和旋转矩阵，而不依赖其他额外的校正设备或人为介入处理. 通过在室内环境中安装两台摆放于不同位置与朝向的深度摄影机，从而，验证了该方法的实时性与易用性. 该实时标定方法解决了深度摄影机侦测范围有限的限制，同时，可由两两相邻的标定扩展到多台深度相机的全局标定，从而，可以被广泛的应用于人体行为识别、情境感知服务等领域.Combining depth information and color image, D-RGB cameras provide a ready detection of human and associated 3D skeleton joints data, facilitating, if not revolutionizing, conventional image centric researches in, among others, computer vision, surveillance, and human activity analysis. Applicability of a D-RBG camera, however, is restricted by its limited range of frustum of depth in the range of 0.8 to 4 meters. Although a D-RGB camera network, constructed by deployment of several D-RGB cameras at various locations, could extend the range of coverage, it requires precise localization of the camera network: relative location and orientation of neighboring cameras. By introducing a skeleton-based viewpoint invariant transformation (SVIT), which derives the relative location and orientation of a detected humans upper torso to a D-RGB camera, this paper presents a reliable automatic localization technique without the need for additional instrument or human intervention. By respectively applying SVIT to two neighboring D-RGB cameras on a commonly observed skeleton, the respective relative position and orientation of the detected humans skeleton for these two cameras can be obtained before being combined to yield the relative position and orientation of these two cameras, thus solving the localization problem. Experiments have been conducted in which two Kinects are situated with bearing differences of about 45 degrees and 90 degrees; the coverage can be extended by up to 70% with the installment of an additional Kinect. The same localization technique can be applied repeatedly to a larger number of D-RGB cameras, thus extending the applicability of D-RGB cameras to camera networks in making human behavior analysis and context-aware service in a larger surveillance area.
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Keywords:
- D-RGB camera network /
- network localization /
- viewpoint invariance /
- Kinect
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[1] Xu Y N, Zhao Y, Liu L P, Zhang Y, Sun X D 2010 Acta Phys. Sin. 59 980 (in Chinese) [许元男, 赵远, 刘丽萍, 张宇, 孙秀冬2010 59 980]
[2] Zhang W, Cheng B, Zhang B 2012 Acta Phys. Sin. 61 060701 (in Chinese)[张伟, 成波, 张波2012 61 060701]
[3] [4] Sung J, Ponce C, Selman B, Saxena A 2012 IEEE International Conference on Robotics and Automation Saint Paul, USA, May 14-18 2012 p842
[5] [6] [7] Xia L, Chen C C, Aggarwal J K 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Providence, United states, June 16-21, 2012 p8
[8] Yun K, Honorio J, Chattopadhyay D, Berg T L, Samaras D 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Providence, United states, June 16-21, 2012 p28
[9] [10] Zhang Z Y 1999 Proceedings of the IEEE International Conference on Computer Vision Kerkyra, Greece, September 20-27, 1999 p666
[11] [12] Tsai R Y 1986 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Miami Beach, United states, June 22-26, 1986 p364
[13] [14] [15] Jia Q Q, Wang B X, Shi H, Luo X Z 2009 Journal of Tsinghua University 49 676 (in Chinese) [贾倩倩, 王伯雄, 史辉, 罗秀芝2009 清华大学学报49 676]
[16] [17] Zhang Z Y 2004 IEEE Transactions on Pattern Analysis and Machine Intelligence 26 892
[18] Thomas E, Andreas S 2012 7th German Conference on RoboticsMunich, Germany, May 21-22 2012 p1
[19] [20] [21] Han Y, Chung S L, Yeh J S, Chen Q J 2013 IEEE International Conference on Systems, Man, and Cybernetics Manchester, UK, October 13-16, 2013 p1525
[22] Mi T, An P, Liu S X, Zhang Z Y 2008 Journal of Image and Graphics 13 1921
[23] [24] Zhang T N, Meng C N, Liu R B, Chang S J 2013 Acta Phys. Sin. 62 134204 (in Chinese)[张太宁, 孟春宁, 刘润蓓, 常胜2013 62 134204]
[25] [26] [27] Grimson W E L 1985 IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7 17
[28] Wang F, Zhao X, Yang Y, Fang Z L, Yuan X C 2012 Acta Phys. Sin. 61 084212 (in Chinese)[王芳, 赵星, 杨勇, 方志良, 袁小聪2012 61 084212]
[29] [30] [31] Atiya S, Hager G D 1993 IEEE Transactions on Robotics and Automation 9 785[17] Hoppen P, Knieriemen T, von Puttkamer E 1990 IEEE International Conference on Robotics and Automation Cincinnati, USA, May 13-18 1990 p948
[32] [33] Hoppen P, Knieriemen T, von Puttkamer E 1990 IEEE International Conference on Robotics and Automation Cincinnati, USA, May 1318 1990 p948
[34] Weiss A, Hirshberg D, Black M J 2011 IEEE International Conference on Computer Vision Barcelona, Spain, November 6-13, 2011 p1951
[35] [36] Moeslund T B, Hilton A, Krger V 2006 Computer Vision and Image Understanding 104 90
[37] [38] Poppe R 2007 Computer Vision and Image Understanding 108 4
[39] [40] Shotton J, Fitzgibbon A, Cook M, Sharp T, Finocchio M, Moore R, Kipman A, Blake A 2011 IEEE Conference on Computer Vision and Pattern Recognition Colorado Springs, United states, June 20-25, 2011 p1297
[41] [42] [43] PrimeSense www. primesense. com [June 5, 2013]
[44] [45] Han J G, Shao L, Xu D, Shotton J 2013 IEEE Transactions on Cybernetics 43 1318
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