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The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.


Camera Positioning and Calibration Techniques for Integrating Traffic Surveillance Video Systems with Machine-Vision Vehicle Detection Devices
Pack, Michael L.
Brian L. Smith
Year: 2002
VTRC No.: 03-CR9
Abstract: The Virginia Department of Transportation, like many other transportation agencies, has invested significantly in extensive closed circuit television (CCTV) systems to monitor freeways in urban areas. Although these systems have proven very effective in supporting incident management, they do not support the collection of quantitative measures of traffic conditions. Rather, they simply provide a moveable platform for trained operators to collect images for further interpretation. Although there are several video image vehicle detection systems (VIVDS) on the market that have the capability to derive traffic measures from video imagery automatically, these systems currently require the installation of fixed-position cameras. Thus, they have not been integrated with the existing moveable CCTV cameras. This research effort addressed VIVDS camera repositioning and calibration challenges and developed a prototype machine-vision system that successfully integrates existing moveable CCTV cameras with VIVDS. Results of testing the prototype in a laboratory setting demonstrated that when the camera's original zoom level was at a level of 1x to 1.5x, the system could return the camera to its original position with a repositioning accuracy of less than 0.03 to0.1 degree. This is significantly less that the 0.5-degree accuracy of mechanical camera presets and indicates that such an approach provides the accuracy needed for CCTV/VIVDS integration. This level of positional accuracy, when combined with a VIVDS, resulted in vehicle count errors of less than 1%. Based on these results, the integration of CCTV and VIVDS is feasible, thus paving the way for less costly, more easily maintained traffic monitoring systems in future intelligent transportation system initiatives.