Automated Camera Repositioning Technique for Video Image Vehicle Detection Systems: Integrating with Freeway Closed-Circuit Television Systems

Abstract

Transportation agencies have invested significantly in extensive closed-circuit television (CCTV) systems to monitor freeways in urban areas. While thes systems have proven to be very effective in supporting incident management, they do not support the collection of quantitative measures of traffic conditions. Instead, they simply provide images that must be interpreted by trained operators. While there are several video image vehicle detection systems (VIVDS) on the market that have the capability to automatically derive traffic measures fro video imagery, these systems require the installation of fixed-position cameras. Thus, they have not been integrated with the existing moveable CCTV cameras. VIVDS camera positioning and calibration challenges were addressed and a prototype machine-vision system was developed that successfully integrated existing moveable CCTV cameras with VIVDS. Results of testing the prototype are presentedindicating that when the camera’s initial zoom level was kept between ×1 and ×1.5, the camera consistently could be returned to its original position with a repositioning accuracy of less than 0.03 to 0.1 regardless of the camera’s displaced pan, tilt, or zoom settings at the time of repositioning. This level of positional accuracy when combined with a VIVDS resulted in vehicle count errors of less than 1%.

Effects of Traffic Cameras on Incident Management

“…Evaluating the effects of traffic cameras on incident management has attracted considerable attention recently. Most studies focused on measuring effects that traffic cameras have from a cost-benefit ratio perspective and usually adopted a simulation method (5,11,14,15). However, to the best of the authors' knowledge, few studies have used field data to examine the effects of traffic cameras that act as independent devices.…”

“…For example, in the early stage of CCTV application in transportation, research was focused mainly on describing the advantages of CCTV and the demand for video surveillance; more recently, researchers are trying to use computer technology to develop the robustness of a real-time automated surveillance system, such as the video image vehicle-detection system (1)(2)(3). This system attempts to detect, recognize, and track objects of interest from video cameras automatically (4,5). To the best of the authors' knowledge, the literature has not examined the statistically significant effects of traffic cameras on traffic incident management.…”

A Synthesis of emerging data collection technologies and their impact on traffic management applications

“…These systems may have high initial costs, but require little maintenance (which minimizes traffic disruptions). They also do not suffer from the reliability issues associated with loop detectors [53]. However, the performance of video image detection systems are affected by adverse weather conditions (such as rain, fog) and limited visibility (e.g.…”

Video-Based Automated Identification of Freeway Shoulder Events

“…The University of Virginia Center for Transportation Studies, sponsored by VDOT, developed a prototype information system that integrates CCTV and VIVDS, allowing for traffic data collection from movable CCTV cameras. This system, referred to as AutoTrack, is fully described in work previously published by TRB (1,2). While working with VDOT field personnel on the prototype system, it became clear that an application of growing importance for CCTV is safety and security.…”