Fast non-parametric action recognition

In this work we propose a method for action recognition which needs no intensive learning stage, and achieves state-of-the-art classification performance. Our work is based on a method presented in the context of image classification. Unlike that method, our approach is well-suited for working with...

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Autores principales: Ubalde, S., Goussies, N.A.
Formato: SER
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Acceso en línea:http://hdl.handle.net/20.500.12110/paper_03029743_v7441LNCS_n_p268_Ubalde
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spelling todo:paper_03029743_v7441LNCS_n_p268_Ubalde2023-10-03T15:19:27Z Fast non-parametric action recognition Ubalde, S. Goussies, N.A. action recognition image-to-class distance nearest neighbor Action recognition Average running time Classification performance Data sets image-to-class distance Nearest neighbors Non-parametric Real-world problem Training data Image analysis Computer vision In this work we propose a method for action recognition which needs no intensive learning stage, and achieves state-of-the-art classification performance. Our work is based on a method presented in the context of image classification. Unlike that method, our approach is well-suited for working with large real-world problems, thanks to an efficient organization of the training data. We show results on the KTH and IXMAS datasets. On the challenging IXMAS dataset, the average running time is reduced by 50% when using our method. © 2012 Springer-Verlag. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v7441LNCS_n_p268_Ubalde
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic action recognition
image-to-class distance
nearest neighbor
Action recognition
Average running time
Classification performance
Data sets
image-to-class distance
Nearest neighbors
Non-parametric
Real-world problem
Training data
Image analysis
Computer vision
spellingShingle action recognition
image-to-class distance
nearest neighbor
Action recognition
Average running time
Classification performance
Data sets
image-to-class distance
Nearest neighbors
Non-parametric
Real-world problem
Training data
Image analysis
Computer vision
Ubalde, S.
Goussies, N.A.
Fast non-parametric action recognition
topic_facet action recognition
image-to-class distance
nearest neighbor
Action recognition
Average running time
Classification performance
Data sets
image-to-class distance
Nearest neighbors
Non-parametric
Real-world problem
Training data
Image analysis
Computer vision
description In this work we propose a method for action recognition which needs no intensive learning stage, and achieves state-of-the-art classification performance. Our work is based on a method presented in the context of image classification. Unlike that method, our approach is well-suited for working with large real-world problems, thanks to an efficient organization of the training data. We show results on the KTH and IXMAS datasets. On the challenging IXMAS dataset, the average running time is reduced by 50% when using our method. © 2012 Springer-Verlag.
format SER
author Ubalde, S.
Goussies, N.A.
author_facet Ubalde, S.
Goussies, N.A.
author_sort Ubalde, S.
title Fast non-parametric action recognition
title_short Fast non-parametric action recognition
title_full Fast non-parametric action recognition
title_fullStr Fast non-parametric action recognition
title_full_unstemmed Fast non-parametric action recognition
title_sort fast non-parametric action recognition
url http://hdl.handle.net/20.500.12110/paper_03029743_v7441LNCS_n_p268_Ubalde
work_keys_str_mv AT ubaldes fastnonparametricactionrecognition
AT goussiesna fastnonparametricactionrecognition
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