Design of a multiple-target based automated camera repositioning system for integrating CCTV with video image vehicle detection systems

2005 ◽  
Author(s):  
H. Tanikella ◽  
S. Namkoong ◽  
B.L. Smith
Keyword(s):  
Vehicle Detection ◽  
Video Image ◽  
Multiple Target ◽  
Detection Systems

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

2003 ◽  
Vol 1856 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Michael L. Pack ◽  
Brian L. Smith ◽  
William T. Scherer
Keyword(s):  
Urban Areas ◽  
Vision System ◽  
Vehicle Detection ◽  
Video Image ◽  
Closed Circuit ◽  
Original Position ◽  
Incident Management ◽  
Positional Accuracy ◽  
Closed Circuit Television ◽  
Detection Systems

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%.

Expanding the Capabilities of Radar-Based Vehicle Detection Systems: Noise Characterization and Removal Procedures

2019 ◽  
Vol 2673 (11) ◽  
pp. 150-160
Author(s):  
Kelvin R. Santiago-Chaparro ◽  
David A. Noyce
Keyword(s):  
Performance Measures ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Noise Removal ◽  
Performance Measure ◽  
Signalized Intersections ◽  
Volume Data ◽  
Trajectory Data ◽  
Potential Accuracy ◽  
Detection Systems

The capabilities of radar-based vehicle detection (RVD) systems used at signalized intersections for stop bar and advanced detection are arguably underutilized. Underutilization happens because RVD systems can monitor the position and speed (i.e., trajectory) of multiple vehicles at the same time but these trajectories are only used to emulate the behavior of legacy detection systems such as inductive loop detectors. When full vehicle trajectories tracked by an RVD system are collected, detailed traffic operations and safety performance measures can be calculated for signalized intersections. Unfortunately, trajectory datasets obtained from RVD systems often contain significant noise which makes the computation of performance measures difficult. In this paper, a description of the type of trajectory datasets that can be obtained from RVD systems is presented along with a characterization of the noise expected in these datasets. Guidance on the noise removal procedures that can be applied to these datasets is also presented. This guidance can be applied to the use of data from commercially-available RVD systems to obtain advanced performance measures. To demonstrate the potential accuracy of the noise removal procedures, the procedures were applied to trajectory data obtained from an existing intersection, and data on a basic performance measure (vehicle volume) were extracted from the dataset. Volume data derived from the de-noised trajectory dataset was compared with ground truth volume and an absolute average difference of approximately one vehicle every 5 min was found, thus highlighting the potential accuracy of the noise removal procedures introduced.

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