A neural network based vehicle detection and tracking system
S. Mantri, Darcy Michael Bullock · 2002
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.>