  In this paper, a novel cognitive architecture for action recognition is
developed by applying layers of growing grid neural networks.Using these layers
makes the system capable of automatically arranging its representational
structure. In addition to the expansion of the neural map during the growth
phase, the system is provided with a prior knowledge of the input space, which
increases the processing speed of the learning phase. Apart from two layers of
growing grid networks the architecture is composed of a preprocessing layer, an
ordered vector representation layer and a one-layer supervised neural network.
These layers are designed to solve the action recognition problem. The
first-layer growing grid receives the input data of human actions and the
neural map generates an action pattern vector representing each action sequence
by connecting the elicited activation of the trained map. The pattern vectors
are then sent to the ordered vector representation layer to build the
time-invariant input vectors of key activations for the second-layer growing
grid. The second-layer growing grid categorizes the input vectors to the
corresponding action clusters/sub-clusters and finally the one-layer supervised
neural network labels the shaped clusters with action labels. Three experiments
using different datasets of actions show that the system is capable of learning
to categorize the actions quickly and efficiently. The performance of the
growing grid architecture is com-pared with the results from a system based on
Self-Organizing Maps, showing that the growing grid architecture performs
significantly superior on the action recognition tasks.
