1. A moving object detection method based on a Probabilistic Neural Network (PNN) model to distinguish a moving object pixel from a background object pixel in a video image delivered over a bandwidth limited network, and the method comprising: receiving a sequence of images of a fixed location; generating from the sequence of images a background model based on the PNN model having four layers comprising an input units layer, a pattern units layer, a summation units layer, and an output units layer; defining the nodes of the four layers of the PNN model, the weight coefficient for each of the nodes, and the relationships among the nodes, wherein defining the nodes of the pattern units layer comprises: receiving pixel intensity p t (x, y) for each pixel of each t-th frame of the sequence of images, wherein the pixel intensity p t (x, y) for each pixel has L possible levels; generating a histogram distribution for each t-th frame of pixel intensity p t (x, y) of all pixels, and the distribution is represented by D(p t (x, y) k )={n 0 , n 1 ,. .. , n k ,. .. , n L-1 }, wherein n k is the number of pixels whose level is p t (x, y) for each intensity level from 0 to k, and D(p t (x, y) k ) is the distribution; and defining i-th node and assigning weight coefficients, C t (x, y) i , for the i-th node according to P t ⁡ ( x , y ) k ⁢ { ∈ C t ⁡ ( x , y ) i , if ⁢ ⁢ D ⁡ ( p t ⁡ ( x , y ) k ) ≥ ɛ ∉ C t ⁡ ( x , y ) i , if ⁢ ⁢ D ⁡ ( p t ⁡ ( x , y ) k ) < ɛ where k=0 to L−1, and ε is a predetermined threshold; receiving a current frame of the sequence of images and dividing the current frame into N by N blocks where N is a positive integer greater than 1; determining pixel blocks by eliminating from the N by N blocks which are statistically determined to contain only background pixels; and determining whether a pixel of the pixel blocks of the sequence of images is a moving object pixel or a background object pixel by comparing the maximum value of all nodes of the output units layer against an output threshold.