Video-Based Automated Identification of Freeway Shoulder Events

Abstract

Transportation agencies have increasingly turned to information technology to help address the growing challenges of managing congested urban freeways. Although off-the-shelf information technology products, such as closed-circuit television (CCTV) systems and video image vehicle-detection systems (VIVDS), have proved very useful, their full potential cannot be achieved without integrating the products. The purpose of this research effort was to build on an existing prototype of an integrated CCTV–VIVDS system to provide automatic shoulder event identification functionality. This paper presents the design of the integrated system, provides details on the tools and effort required to support integration of the information technology elements, and describes the results of field testing of the system.

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References

Pack

M. L.

Smith

B. L.

and Scherer

W. T.

. An Automated Camera Repositioning Technique for Integrating Video Image Vehicle Detection Systems: Integrating with Freeway Closed-Circuit Television Systems. In Transportation Research Record: Journal of the Transportation Research Board, No. 1856, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 25–33.

Namkoong

S. J.

Tanikella

and Smith

B. L.

. Design and Field Evaluation of System Integrating Closed-Circuit Television Surveillance with Video Image Vehicle Detection Systems. Presented at 84th Annual Meeting of the Transportation Research Board, Washington, D.C., 2005.

Middleton

Parker

Urbanik

Kuhn

Balke

and Jasek

. Vehicle Detection Workshop. Texas Transportation Institute, Texas A&M University System, College Station, 2000.

Bouzar

and Toffin

. Active Video Surveillance of Traffic on PTZ Cameras. Intelligent Transportation Systems Safety and Security Conference, 2004.

Agarwal

Andrisevic

Vuppla

and Stachowicz

. Vehicle Detection and Tracking in Video for Incident Detection. University of Minnesota, Duluth, 2004.

Rajagopalan

A. N.

and Chellappa

. Vehicle Detection and Tracking in Video Center for Automation Research. University of Maryland, College Park, 2000.

Schoepflin

T. N.

and Dailey

D. J.

. Cross-Correlation Tracking Technique for Extracting Speed from Cameras Under Adverse Conditions. In Transportation Research Record: Journal of the Transportation Research Board, No. 1867, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 36–45.

Autoscope Solo Pro User Guide. PN 2800-00E. Image Sensing Systems, Inc., St. Paul, Minn., 2003.