Using geometric primitives to calibrate traffic scenes

In this paper, we address the problem of recovering the intrinsic and extrinsic parameters of a camera or a group of cameras in a setting overlooking a traffic scene. Unlike many other settings, conventional camera calibration techniques are not applicable in this case. We present a method that uses certain geometric primitives commonly found in traffic scenes in order to recover calibration parameters. These primitives provide needed redundancy and are weighted depending on the significance of there corresponding image features. We show experimentally that these primitives are capable of achieving accurate results suitable for most traffic monitoring applications.

[1]  José Santos-Victor,et al.  Algebraic Aspects of Reconstruction of Structured Scenes from One or More Views , 2001 .

[2]  Ginés García-Mateos,et al.  A note on principal point estimability , 2002, Object recognition supported by user interaction for service robots.

[3]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[4]  Gilles Trombettoni,et al.  Scene modeling based on constraint system decomposition techniques , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[5]  Adrien Bartoli,et al.  Constrained Structure and Motion From Multiple Uncalibrated Views of a Piecewise Planar Scene , 2003, International Journal of Computer Vision.

[6]  ZhangZhengyou Estimating Motion and Structure from Correspondences of Line Segments between Two Perspective Images , 1995 .

[7]  David J. Kriegman,et al.  Structure and Motion from Line Segments in Multiple Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Minas E. Spetsakis,et al.  Structure from motion using line correspondences , 1990, International Journal of Computer Vision.

[9]  Andrew Zisserman,et al.  Metric rectification for perspective images of planes , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[10]  Paul Debevec,et al.  Modeling and Rendering Architecture from Photographs , 1996, SIGGRAPH 1996.

[11]  Jitendra Malik,et al.  Modeling and Rendering Architecture from Photographs: A hybrid geometry- and image-based approach , 1996, SIGGRAPH.

[12]  Bernard Mourrain,et al.  An Application of Automatic Theorem Proving in Computer Vision , 1998, Automated Deduction in Geometry.

[13]  Robert T. Collins,et al.  Vanishing point calculation as a statistical inference on the unit sphere , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[14]  Geoffrey D. Sullivan,et al.  A Simple, Intuitive Camera Calibration Tool for Natural Images , 1994, BMVC.

[15]  Pierre Gurdjos,et al.  About conditions for recovering the metric structures of perpendicular planes from the single ground plane to image homography , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[16]  Adrien Bartoli,et al.  Motion from 3D line correspondences: linear and nonlinear solutions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[17]  金谷 健一 Statistical optimization for geometric computation : theory and practice , 2005 .

[18]  Pierre-Louis Bazin Parametric scene reduction algorithm from geometric relations , 2000, SPIE Optics + Photonics.

[19]  Zhengyou Zhang Estimating Motion and Structure from Correspondences of Line Segments between Two Perspective Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Roberto Cipolla,et al.  3D models of architectural scenes from uncalibrated images and vanishing points , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[21]  Ian D. Reid,et al.  Single View Metrology , 2000, International Journal of Computer Vision.