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Learning sparse representations for view-independent human action recognition based on fuzzy distances

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)344-353
Number of pages10
JournalNeurocomputing
Volume121
DOIs
Publication statusPublished - 9 Dec 2013
Publication typeA1 Journal article-refereed

Abstract

In this paper, a method aiming at view-independent human action recognition is presented. Actions are described as series of successive human body poses. Action videos representation is based on fuzzy vector quantization, while action classification is performed by a novel classification algorithm, the so-called Sparsity-based Learning Machine (SbLM), involving two optimization steps. The first one determines a non-linear data mapping to a high-dimensional feature space determined by an l1-minimization process exploiting an overcomplete dictionary formed by the training samples. The second one, involves a training process in order to determine the optimal separating hyperplanes in the resulted high-dimensional feature space. The performance of the proposed human action recognition method is evaluated on two publicly available action recognition databases aiming at different application scenarios.

Keywords

  • Action classification, Activity recognition, Fuzzy vector quantization, Sparse data representation