Neural network classifiers for automated video surveillance

In automated visual surveillance applications, detection of suspicious human behaviors is of great practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Artificial neural network (ANN) classifiers can perform well however their computational requirements can be very large for real time implementation. In this paper, a data-based modeling neural network such as modified probabilistic neural network (MPNN) is introduced which partitions the decision space nonlinearly in order to achieve reliable classification, however still with acceptable computations. The experiment shows that the compact MPNN attains good classification performance compared to that of other larger conventional neural network based classifiers such as multilayer perceptron (MLP) and self organising map (SOM).