Common-sense reasoning for human action recognition

  • Authors:

  • Jesús Martínez Del Rincón;Maria J. Santofimia;Jean-Christophe Nebel

  • Affiliations:

  • The Institute of Electronics, Communications and Information Technology (ECIT), Queen's University of Belfast, BT3 9DT, UK;Department of Technology and Information Systems, Computer Engineering School, University of Castilla-La Mancha, Ciudad Real, Spain;Digital Imaging Research Centre, Kingston University, London, KT1 2EE, UK

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2013

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Abstract

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm - known as bag of words - gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline.