Abstract
Outdoor visual surveillance scenes usually contain lots of objects moving on a ground plane. However, the perspective distortion brings in the result that the same object moves faster and looks larger when it is close to the camera, which makes the primary surveillance scenes pictures can’t be used for further research directly. For example, accurate map-making, precise measurement of distance or angles, 3D model estimation and recovery and so on. Therefore, some kind of methods should be provided to eliminate the perspective distortion. In this paper, we make full use of target recognition such as moving vehicles in a video to accomplish rectification. First, we separated the moving targets from the background, then, we detected a lot of line segments from the moving vehicles in each frame, and calculated the vanishing points with parallel line segments and calculated the affine matrix with perpendicular lines, and then, we performed linear regression on the vanishing points and get the vanishing line, at last, we have the perspective matrix and affine matrix calculated and do the rectification to the whole surveillance scene.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zhang, Z., Li, M., Huang, K., Tan, T., et al.: View independent object classification based on automated ground plane rectification for traffic scene surveillance (2008)
Lourakis, M.: Plane metric rectification from a single view of multiple coplanar circles. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 509–512. IEEE (2009)
Torii, A., Havlena, M., Pajdla, T.: Omnidirectional Image Stabilization by Computing Camera Trajectory. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 71–82. Springer, Heidelberg (2009)
Zhang, Z., Li, M., Huang, K., Tan, T.: Robust automated ground plane rectification based on moving vehicles for traffic scene surveillance. In: 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 1364–1367. IEEE (2008)
Deutscher, J., Isard, M., MacCormick, J.: Automatic Camera Calibration from a Single Manhattan Image. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part IV. LNCS, vol. 2353, pp. 175–188. Springer, Heidelberg (2002)
Everingham, M., Zisserman, A., Williams, C., Van Gool, L., Allan, M., Bishop, C., Chapelle, O., Dalal, N., Deselaers, T., Dorkó, G., et al.: The 2005 pascal visual object classes challenge. In: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment, pp. 117–176 (2006)
Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision 63(2), 153–161 (2005)
Rasmussen, C.: The infinite gaussian mixture model
Galamhos, C., Matas, J., Kittler, J.: Progressive probabilistic hough transform for line detection. In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Z., Zhang, Z., Wang, Y., Li, X., Wang, C. (2012). Ground Plane Rectification Based on Rich Line Representation of Vehicle in Surveillance. In: Liu, CL., Zhang, C., Wang, L. (eds) Pattern Recognition. CCPR 2012. Communications in Computer and Information Science, vol 321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-33506-8_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33505-1
Online ISBN: 978-3-642-33506-8
eBook Packages: Computer ScienceComputer Science (R0)