Common-sense reasoning for human action recognition

J. Martínez del Rincón, Maria J. Santofimia, Jean-Christophe Nebel

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12 Citations (Scopus)
308 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1849-1860
Number of pages12
JournalPattern Recognition Letters
Issue number15
Early online date21 Nov 2012
Publication statusPublished - 01 Nov 2013

Bibliographical note

Copyright 2012 Elsevier B.V., All rights reserved.

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