Effective Surveillance Image Analysis Using Combination of Linear Regression Model and Modified Probabilistic Neural Networks
T Jan, Arjun Krishna, Massimo Piccardi, T. Hintz · UTS ePRESS (University of Technology Sydney) · 2002
In this paper, a hybrid model is introduced which combines a linear regression model in parallel with a nonlinear regression model such as the Modified Probabilistic Neural Network (MPNN).This model provides a first order approximation of the underlying mechanism using linear regression, and then use the MPNN to capture the local details of interest.This model allows the selected data regions of interest be modeled more accurately by a nonlinear compensator while the rest of the data regions are approximated by a linear regression model.The experiment on surveillance image modelling shows that the proposed model achieves improved performance over conventional methods such as MultiLayer Perceptron (MLP) or Volterra Filter based modelling.