Capabilities and limitations of mono-camera pedestrian-based autocalibration

Many environments lack enough architectural information to allow an autocalibration based on features extracted from the scene structure. Nevertheless, the observation over time of walking people can generally be used to estimate the vertical vanishing point and the horizon line in the acquired image. However, this information is not enough to allow the calibration of a general camera without presuming excessive simplifications. This paper presents a study on the capabilities and limitations of the mono-camera calibration methods based solely on the knowledge of the vertical vanishing point and the horizon line in the image. The mathematical analysis sets the conditions to assure the feasibility of the mono-camera pedestrian-based autocalibration. In addition, examples of applications are presented and discussed.

[1]  Ramakant Nevatia,et al.  Camera calibration from video of a walking human , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  H. Opower.  Multiple view geometry in computer vision , 2002 .

[3]  James H. Elder,et al.  Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery , 2008, ECCV.

[4]  Paulo R. S. Mendonça,et al.  Bayesian autocalibration for surveillance , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[5]  Paulo R. S. Mendonça,et al.  Autocalibration from Tracks of Walking People , 2006, BMVC.

[6]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Imran N. Junejo.  Using pedestrians walking on uneven terrains for camera calibration , 2009, Machine Vision and Applications.

[8]  Guanghui Wang,et al.  What can we learn about the scene structure from three orthogonal vanishing points in images , 2009, Pattern Recognit. Lett..

[9]  Honglak Lee,et al.  A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .