1. A moving object detection method based on a Principal Component Analysis-based Radial Basis Function network (PCA-based RBF network) comprising: receiving a sequence of incoming frames of a fixed location delivered over a network; generating a plurality of Eigen-patterns from the sequence of incoming frames based on a Principal Component Analysis (PCA) model, wherein the PCA model comprises an optimal projection vector; constructing a background model from the sequence of incoming frames based on a Radial Basis Function (RBF) network model, wherein the RBF network model comprises an input layer having a plurality of input layer neurons, a hidden layer having a plurality of hidden layer neurons, and an output layer having an output layer neuron, and wherein there exists a weight between each of the hidden layer neurons and the output layer neuron; receiving a current incoming frame delivered over the network and partitioning the current incoming frame into a plurality of current incoming blocks; classifying each of the current incoming blocks as either a background block or a moving object block according to the Eigen-patterns; and determining whether a current incoming pixel of the moving object blocks among the current incoming blocks is a moving object pixel or a background pixel according to the background model.