Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Hierarchies of Self-Organizing Maps for Action Recognition

In English

Författare

  • Miriam Buonamente
  • Haris Dindo
  • Magnus Johnsson

Summary, in English

We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and learns to represent action prototypes. The third - and last - layer of the hierarchy consists of a neural network that learns to label action prototypes of the second-layer SOM and is independent - to certain extent - of the camera’s angle and relative distance to the actor. The experiments were carried out with encouraging results with action movies taken from the INRIA 4D repository. In terms of representational accuracy, measured as the recognition rate over the training set, the architecture exhibits 100% accuracy indicating that actions with overlapping patterns of activity can be correctly discriminated. On the other hand, the architecture exhibits 53% recognition rate when presented with the same actions interpreted and performed by a different actor. Experiments on actions captured from different view points revealed a robustness of our system to camera rotation. Indeed, recognition accuracy was comparable to the single viewpoint case. To further assess the performance of the system we have also devised a behavioral experiments in which humans were asked to recognize the same set of actions, captured from different points of view. Results form such a behavioral study let us argue that our architecture is a good candidate as cognitive model of human action recognition, as architectural results are comparable to those observed in humans.

Publiceringsår

2016

Språk

Engelska

Publikation/Tidskrift/Serie

Cognitive Systems Research

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Human Computer Interaction

Nyckelord

  • Self-Organizing Map
  • Neural Network
  • Action Recognition
  • Hierarchical models
  • Intention Understanding

Status

Published

Projekt

  • What you say is what you did (WYSIWYD)
  • Ikaros: An infrastructure for system level modelling of the brain

Forskningsgrupp

  • Lund University Cognitive Science (LUCS)

ISBN/ISSN/Övrigt

  • ISSN: 1389-0417