Method and apparatus for moving object detection using fisher's linear discriminant based radial basis function network

A moving object detection apparatus, comprising a processor and a memory, wherein the processor is coupled to the memory and is configured for:

• receiving a sequence of incoming frames of a fixed location delivered over a network;
• generating a plurality of discriminant patterns from the sequence of incoming frames based on a Fisher's Linear Discriminant (FLD) model having an optimal projection vector, comprising:
• dividing each of the incoming frames into a plurality of training blocks and classifying the training blocks into a plurality of classes;
• calculating a between-class scatter matrix and a within-class scatter matrix according to the training blocks;
• calculating the optimal projection vector by maximizing the ratio of the within-class scatter matrix and the between-class scatter matrix;
• and
• obtaining each of the discriminant patterns according to the optimal projection vector and the corresponding training block;
• constructing a background model from the sequence of incoming frames based on a Radial Basis Function Network (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 dividing 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 discriminant patterns generated from the sequence of incoming frames based on the FLD model, comprising:
• calculating a projection of each of the current incoming blocks according to the optimal projection vector;
• calculating a similarity level between the discriminant pattern and the projection of each of the current incoming blocks;
• determining if the similarity level exceeds the second threshold value;
• if yes, classifying the current incoming block as the background block;
• and
• if no, classifying the current incoming block as the moving object block;
• 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.