Abstract:
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).
