Visual tracking for intelligent vehicle-highway systems

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 se...

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Bibliographic Details
Published inIEEE transactions on vehicular technology Vol. 45; no. 4; pp. 744 - 759
Main Authors Smith, C.E., Richards, C.A., Brandt, S.A., Papanikolopoulos, N.P.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.11.1996
Institute of Electrical and Electronics Engineers
Subjects
Application software
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Computer vision
Exact sciences and technology
Intelligent sensors
Intelligent systems
Intelligent vehicles
Pattern recognition. Digital image processing. Computational geometry
Robustness
Sensor systems
Transportation
Vehicle detection
Vehicle safety
Tracking(movable target)
Traffic control
Computer vision
Image processing
Artificial intelligence
Target detection
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Summary: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. 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 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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/25.543744