HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system

  • Authors:

  • Siu-Yeung Cho;Chai Quek;Shao-Xiong Seah;Chin-Hui Chong

  • Affiliations:

  • Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singapore 639798, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Blk N4 #2A-32, Nanyang Avenue, Singapore 639798, Singapore

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

This paper presents a robust methodology that automatically counts moving vehicles along an expressway. The domain of interest for this paper is using both neuro-fuzzy network and simple image processing techniques to implement traffic flow monitoring and analysis. As this system is dedicated for outdoor applications, efficient and robust processing methods are introduced to handle both day and night analysis. In our study, a neuro-fuzzy network based on the Hebbian-Mamdani rule reduction architecture is used to classify and count the number of vehicles that passed through a three- or four-lanes expressway. As the quality of the video captured is corrupted under noisy outdoor environment, a series of preprocessing is required before the features are fed into the network. A vector of nine feature values is extracted to represent whether a vehicle is passing through a lane and this vector serves as input patterns would be used to train the neuro-fuzzy network. The vehicle counting and classification would then be performed by the well-trained network. The novel approach is benchmarked against the MLP and RBF networks. The results of using our proposed neuro-fuzzy network are very encouraging with a high degree of accuracy.