+ABCDEFGHIJKL"MNOPQRMSTUVHWXYZ[\]^_`abcdJKefag3h9ijk. lmJen"Xop(qQRlrstuJWvMhwxJyz{Y|JKW(post production)}^lrs~?XWH4Hk. l{3ySR X, )n7+V`k. HVXMSTU|M^yvJKIHdVK^d3,Xi, HV-XpJKag3hW(POVIS: post virtual imaging system)Xk. Qlrsf7~KW7;HIH ii[¢XW, HV£¤¥¦§LHaXpRX93ig3hWHI ¨aXk. ©KHdWv7~a}^`«R¬ V®X9iW, Kalman ¯dVHaXpHV°HW ¨Xk. _\±POVISWk²³dJai[RF9´µo, ¶·¸:^`¹ ¨ag3oWySJK?*}^3iXk.AbstractReal-time virtual studios which could run only on expensive workstations are now available for personal computers thanks to the recent development of graphics hardware. Nevertheless, graphics are rendered off-line in the post production stage in film or TV drama productions, because the graphics' quality is still restricted by the real-time hardware. Software-based camera tracking methods taking only the source video into account take much computation time, and often shows unstable results. To overcome this restriction, we propose a system that stores camera motion data from sensors at shooting time as common virtual studios and uses them in the post production stage, named as POVIS(post virtual imaging system). For seamless registration of graphics onto the camera video, precise zoom lens calibration must precede the post production. A practical method using only two planar patterns is used in this work. We present a method to reduce the camera sensor's error due to the mechanical mismatch, using the Kalman filter. POVIS was successfully used to track the camera in a documentary production and saved much of the processing time, while conventional methods failed due to lack of features to track.
[1]
Jean-Marc Lavest,et al.
Some Aspects of Zoom Lens Camera Calibration
,
1996,
IEEE Trans. Pattern Anal. Mach. Intell..
[2]
Yi-Ping Hung,et al.
Simple and efficient method of calibrating a motorized zoom lens
,
2001,
Image Vis. Comput..
[3]
Bernhard P. Wrobel,et al.
Multiple View Geometry in Computer Vision
,
2001
.
[4]
Elsayed E. Hemayed,et al.
A survey of camera self-calibration
,
2003,
Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..
[5]
Zhengyou Zhang,et al.
A Flexible New Technique for Camera Calibration
,
2000,
IEEE Trans. Pattern Anal. Mach. Intell..
[6]
Kevin L. Moore,et al.
An Analytical Piecewise Radial Distortion Model for Precision Camera Calibration
,
2003,
ArXiv.
[7]
Christian Breiteneder,et al.
Virtual Studios: An Overview
,
1998,
IEEE Multim..
[8]
Hongdong Li,et al.
An LMI Approach for Reliable PTZ Camera Self-Calibration
,
2006,
2006 IEEE International Conference on Video and Signal Based Surveillance.
[9]
J. L. Roux.
An Introduction to the Kalman Filter
,
2003
.
[10]
G C Dean,et al.
An Introduction to Kalman Filters
,
1986
.