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JAR-Aibo: A multi-view dataset for evaluation of model-free action recognition systems

  • Friedrich Schiller University Jena

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel multi-view dataset for evaluating model-free action recognition systems. Superior to existing datasets, it covers 56 distinct action classes. Each of them was performed ten times by remotely controlled Sony ERS-7 AIBO robot dogs observed by six distributed and synchronized cameras at 17 fps and VGA resolution. In total, our dataset contains 576 sequences. Baseline results show its applicability for benchmarking model-free action recognition methods.

Original languageEnglish
Title of host publicationNew Trends in Image Analysis and Processing - ICIAP 2013 International Workshops, Proceedings
PublisherSpringer Verlag
Pages527-535
Number of pages9
ISBN (Print)9783642411892
DOIs
StatePublished - 2013
Externally publishedYes
Event17th International Conference on Image Analysis and Processing, ICIAP 2013 - Naples, Italy
Duration: 9 Sep 201313 Sep 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8158 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Image Analysis and Processing, ICIAP 2013
Country/TerritoryItaly
CityNaples
Period9/09/1313/09/13

Keywords

  • action recognition
  • behaviour understanding
  • dataset

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