Camera positioning and calibration techniques for integrating traffic surveillance video systems with machine-vision vehicle detection devices.

51254303Final report. Sept. 2001-Oct. 2002PDFTech Reporthttp://ntl.bts.gov/lib/19000/19900/19954/03-CR9.pdfVTRC-03-CR960226Video camerasAutomatic vehicle detection and identification systemsVirginiaVirginia Transportation Research CouncilSmith, B. L.Pack, M. L.United States. Federal Highway AdministrationVirginia. Dept. of TransportationNTL-INTELLIGENT TRANSPORTATION SYSTEMS-INTELLIGENT TRANSPORTATION SYSTEMSNTL-INTELLIGENT TRANSPORTATION SYSTEMS-Freeway ManagementNTL-OPERATIONS AND TRAFFIC CONTROLS-OPERATIONS AND TRAFFIC CONTROLSUS Transportation CollectionThe 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