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

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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

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