By Topic

An efficient system for vehicle tracking in multi-camera networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Michael Dixon ; Washington University, St. Louis, MO, USA ; Nathan Jacobs ; Robert Pless

The recent deployment of very large-scale camera networks has led to a unique version of the tracking problem whose goal is to detect and track every vehicle within a large urban area. To address this problem we exploit constraints inherent in urban environments (i.e. while there are often many vehicles, they follow relatively consistent paths) to create novel visual processing tools that are highly efficient in detecting cars in a fixed scene and at connecting these detections into partial tracks.We derive extensions to a network flow based probabilistic data association model to connect these tracks between cameras. Our real time system is evaluated on a large set of ground-truthed traffic videos collected by a network of seven cameras in a dense urban scene.

Published in:

Distributed Smart Cameras, 2009. ICDSC 2009. Third ACM/IEEE International Conference on

Date of Conference:

Aug. 30 2009-Sept. 2 2009