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A neural network based vehicle detection and tracking system

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2 Author(s)
Mantri, S. ; Louisiana State Univ., Baton Rouge, LA, USA ; Bullock, D.

Recent research has shown that feedforward neural networks can be trained to monitor vehicles on the roads (D. Bullock et al., 1993). A properly trained network should be able to recognize vehicles in the images it has never been exposed to. The paper discusses the development of such a neural network based detection and tracking model. The detection and tracking model was constructed on a PC using video tapes of traffic. A hybrid system architecture was developed to provide the necessary interface between the software and hardware modules. Two types of neural networks were investigated: standard feedforward networks and radial basis function (RBF) networks. Various tests were conducted to determine the optimal network model. The RBF network performed better than the conventional feedforward model. A success rate of 93% was achieved with the RBF network based detector model

Published in:

System Theory, 1995., Proceedings of the Twenty-Seventh Southeastern Symposium on

Date of Conference:

12-14 Mar 1995