Hierarchical growing grid networks for skeleton based action recognition

TL;DR

This paper introduces a hierarchical growing grid neural network architecture for skeleton-based action recognition, which automatically structures its representation, incorporates prior knowledge for faster learning, and outperforms traditional self-organizing map systems.

Contribution

The novel hierarchical growing grid architecture enhances action recognition by automatic structure adaptation and improved learning speed, outperforming existing self-organizing map approaches.

Findings

The system learns to categorize actions quickly and efficiently.

It significantly outperforms self-organizing map-based systems.

The architecture effectively handles skeleton-based action data.

Abstract

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.