Novel Multi-Feature Bag-of-Words Descriptor via Subspace Random Projection for Efficient Human-Action Recognition

Maqueda Nieto, Ana I. and Ruano San Martín, Arturo and Blanco Adán, Carlos Roberto del and Carballeira López, Pablo and Jaureguizar Núñez, Fernando and García Santos, Narciso (2015). Novel Multi-Feature Bag-of-Words Descriptor via Subspace Random Projection for Efficient Human-Action Recognition. In: "12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)", 25/08/2015 - 28/08/2015, Karlsruhe, Germany. pp. 1-6. https://doi.org/10.1109/AVSS.2015.7301736.

Description

Title: Novel Multi-Feature Bag-of-Words Descriptor via Subspace Random Projection for Efficient Human-Action Recognition
Author/s:
  • Maqueda Nieto, Ana I.
  • Ruano San Martín, Arturo
  • Blanco Adán, Carlos Roberto del
  • Carballeira López, Pablo
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
Item Type: Presentation at Congress or Conference (Article)
Event Title: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)
Event Dates: 25/08/2015 - 28/08/2015
Event Location: Karlsruhe, Germany
Title of Book: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)
Date: 2015
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Human-action recognition through local spatio-temporal features have been widely applied because of their simplicity and its reasonable computational complexity. The most common method to represent such features is the well-known Bag-of-Words approach, which turns a Multiple-Instance Learning problem into a supervised learning one, which can be addressed by a standard classifier. In this paper, a learning framework for human-action recognition that follows the previous strategy is presented. First, spatio-temporal features are detected. Second, they are described by HOG-HOF descriptors, and then represented by a Bag of Words approach to create a feature vector representation. The resulting high dimensional features are reduced by means of a subspace-random-projection technique that is able to retain almost all the original information. Lastly, the reduced feature vectors are delivered to a classifier called Citation K-Nearest Neighborhood, especially adapted to Multiple-Instance Learning frameworks. Excellent results have been obtained, outperforming other state-of-the art approaches in a public database.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2010-20412Enhanced 3DTVMinisterio de Economía y CompetitividadUnspecified
Government of SpainTEC2013-48453MR-UHDTVMinisterio de Economía y CompetitividadUnspecified

More information

Item ID: 42760
DC Identifier: http://oa.upm.es/42760/
OAI Identifier: oai:oa.upm.es:42760
DOI: 10.1109/AVSS.2015.7301736
Deposited by: Memoria Investigacion
Deposited on: 16 Jul 2016 10:29
Last Modified: 16 Jul 2016 10:29
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