By Topic

Qualitative traffic analysis using image processing and time-delayed neural network

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

2 Author(s)
S. N. Razavi ; Dept. of Comput. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran ; M. Fathy

We present an online, feature-based approach to estimate traffic qualitative parameters from a sequence of traffic images. Considering the factor of time and attempting to simulate human behavior, a time-delay neural network is used to determine the traffic status through traffic lanes. The acquired frames are divided into a number of blocks based on number of lanes and road boundary coordinates, which are obtained automatically by a part of the system called the road boundary detection system. Two extracted principal features from each block of a lane which are vehicle detector and movement detector will form the input vector of the neural network. The neural network classifies each lane into a level of traffic congestion. The neural network was previously trained with various traffic and different lighting conditions. Finally a description of traffic scene is obtained using descriptions of all lanes.

Published in:

Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on

Date of Conference:

2002