Visual tracking for intelligent vehicle-highway systems

Christopher E. Smith, Charles A. Richards, Scott A. Brandt, Nikolaos P. Papanikolopoulos

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


The complexity and congestion of current transportation systems often produce traffic situations that jeopardize the safety of the people involved. These situations vary from maintaining a safe distance behind a leading vehicle to safely allowing a pedestrian to cross a busy street. Environmental sensing plays a critical role in virtually all of these situations. Of the sensors available, vision sensors provide information that is richer and more complete than other sensors, making them a logical choice for a multisensor transportation system. In this paper we propose robust detection and tracking techniques for intelligent vehicle-highway applications where computer vision plays a crucial role. In particular, we demonstrate that the Controlled Active Vision framework [15] can be utilized to provide a visual tracking modality to a traffic advisory system in order to increase the overall safety margin in a variety of common traffic situations. We have selected two application examples, vehicle tracking and pedestrian tracking, to demonstrate that the framework can provide precisely the type of information required to effectively manage the given traffic situation.

Original languageEnglish (US)
Pages (from-to)744-759
Number of pages16
JournalIEEE Transactions on Vehicular Technology
Issue number4
StatePublished - 1996

Bibliographical note

Funding Information:
Manuscript received September 28, 1994; revised February 13, 1995. This work was supported by the Minnesota Department of Transportation through Contracts 71789-72983-169 and 71789-72447-159, the Center for Transportation Studies through Contract USDOTlDTRS 93-G-0017-01, the National Science Foundation through Contracts 1RI-9410003 and IRI-9502245, the Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative Agreement DAAH04-95-2-0003 and Contract DAAH04-95-C-000, the Department of Energy (Sandia National Laboratories) through Contracts AC-3752D and AL-3021, the McKnight Land-Grant Professorship Program at the University of Minnesota, and the Department of Computer Science of the University of Minnesota. C. E. Smith and N. P. Papanikolopoulos are with the Artificial Intelligence, Robotics, and Vision Lab., Department of Computer Science, University of Minnesota, Minneapolis, MN 55455 USA. C. A. Richards is with Stanford Vision Lab., Stanford University, Stanford, CA 94305-9010 USA. S. A. Brandt is with the Department of Computer Science, University of Colorado-Boulder, Boulder, CO 80309-0430 USA. Publisher Item Identifier S 001 8-9545(96)05470-9.


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