Online recognition of unsegmented actions with hierarchical SOM architecture

Hierarchical transfer learning for online recognition of compound actions

Recognising human actions in real-time can provide users with a natural user interface (NUI) enabling a range of innovative and immersive applications. A NUI application should not restrict users’ movements; it should allow users to transition between actions in quick succession, which we term as compound actions. However, the majority of action recognition researchers have focused on individua...

Recognition of Transitive Actions with Hierarchical Neural Network Learning

The recognition of actions that involve the use of objects has remained a challenging task. In this paper, we present a hierarchical selforganizing neural architecture for learning to recognize transitive actions from RGB-D videos. We process separately body poses extracted from depth map sequences and object features from RGB images. These cues are subsequently integrated to learn action–objec...

Hierarchical Activity Recognition Using Automatically Clustered Actions

The automatic recognition of human activities such as cooking, showering and sleeping allows many potential applications in the area of ambient intelligence. In this paper we show that using a hierarchical structure to model the activities from sensor data can be very beneficial for the recognition performance of the model. We present a two-layer hierarchical model in which activities consist o...

Hierarchical Clustering Analysis with SOM Networks

This work presents a neural network model for the clustering analysis of data based on Self Organizing Maps (SOM). The model evolves during the training stage towards a hierarchical structure according to the input requirements. The hierarchical structure symbolizes a specialization tool that provides refinements of the classification process. The structure behaves like a single map with differ...

Spatial Clustering Using Hierarchical SOM