Research on lane-marking line based camera calibration

In this paper, we present a novel camera calibration method which requires only a few easy attainable lane markings in traffic scenes. All we need to know beforehand are a pair of parallel lane markings with known lane width and either the camera height or the length of a land marking parallel to the road. If the camera height is known a-prior, a set of camera parameters such as the focal length, the tilt angle, and the pan angle can be recovered; if the length of a land marking parallel to the road is known a-prior, not only the above camera parameters, but the camera height can also be recovered. We show experimentally that the proposed method is capable of achieving accurate results in most traffic monitoring applications, including inverse perspective transformation and even 3-D estimation of vehicle dimensions.

[1]  Nelson H. C. Yung,et al.  Vehicle type classification from visual-based dimension estimation , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[2]  Kai-Tai Song,et al.  Dynamic Calibration of Pan–Tilt–Zoom Cameras for Traffic Monitoring , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Osama Masoud,et al.  Using geometric primitives to calibrate traffic scenes , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[4]  Aljoscha Smolic,et al.  3-D reconstruction of a dynamic environment with a fully calibrated background for traffic scenes , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Paola Mello,et al.  Image analysis and rule-based reasoning for a traffic monitoring system , 2000, IEEE Trans. Intell. Transp. Syst..

[6]  Fei-Yue Wang A simple and analytical procedure for calibrating extrinsic camera parameters , 2004, IEEE Trans. Robotics Autom..

[7]  Yunfeng Ai,et al.  On Automatic and Dynamic Camera Calibration based on Traffic Visual Surveillance , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[8]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.

[9]  David Beymer,et al.  A real-time computer vision system for vehicle tracking and traffic surveillance , 1998 .

[10]  Daniel J. Dailey,et al.  Dynamic camera calibration of roadside traffic management cameras for vehicle speed estimation , 2003, IEEE Trans. Intell. Transp. Syst..

[11]  Kuntal Sengupta,et al.  Framework for real-time behavior interpretation from traffic video , 2005, IEEE Transactions on Intelligent Transportation Systems.