research-article

Human action recognition optimization based on evolutionary feature subset selection

Authors:

Alexandros Andre Chaaraoui,

Francisco Flórez-RevueltaAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 1229 - 1236

https://doi.org/10.1145/2463372.2463529

Published: 06 July 2013 Publication History

Abstract

Human action recognition constitutes a core component of advanced human behavior analysis. The detection and recognition of basic human motion enables to analyze and understand human activities, and to react proactively providing different kinds of services from human-computer interaction to health care assistance. In this paper, a feature-level optimization for human action recognition is proposed. The resulting recognition rate and computational cost are significantly improved by means of a low-dimensional radial summary feature and evolutionary feature subset selection. The introduced feature is computed using only the contour points of human silhouettes. These are spatially aligned based on a radial scheme. This definition shows to be proficient for feature subset selection, since different parts of the human body can be selected by choosing the appropriate feature elements. The best selection is sought using a genetic algorithm. Experimentation has been performed on the publicly available MuHAVi dataset. Promising results are shown, since state-of-the-art recognition rates are considerably outperformed with a highly reduced computational cost.

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Cited By

Radek S(2020)Skeleton Action Recognition Based on Singular Value Decomposition2020 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP40778.2020.9191009(1831-1835)Online publication date: Oct-2020
https://doi.org/10.1109/ICIP40778.2020.9191009
Liu ZHuang JWang YCao D(2018)ECoFFeS: A Software Using Evolutionary Computation for Feature Selection in Drug DiscoveryIEEE Access10.1109/ACCESS.2018.28214416(20950-20963)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2821441
Simkanič R(2018)Matrix Descriptor of Changes (MDC): Activity Recognition Based on SkeletonAdvanced Concepts for Intelligent Vision Systems10.1007/978-3-030-01449-0_2(14-25)Online publication date: 24-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-01449-0_2
Show More Cited By

Index Terms

Human action recognition optimization based on evolutionary feature subset selection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 ACM.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Radek S(2020)Skeleton Action Recognition Based on Singular Value Decomposition2020 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP40778.2020.9191009(1831-1835)Online publication date: Oct-2020
https://doi.org/10.1109/ICIP40778.2020.9191009
Liu ZHuang JWang YCao D(2018)ECoFFeS: A Software Using Evolutionary Computation for Feature Selection in Drug DiscoveryIEEE Access10.1109/ACCESS.2018.28214416(20950-20963)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2821441
Simkanič R(2018)Matrix Descriptor of Changes (MDC): Activity Recognition Based on SkeletonAdvanced Concepts for Intelligent Vision Systems10.1007/978-3-030-01449-0_2(14-25)Online publication date: 24-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-01449-0_2
Xue BZhang MBrowne WYao X(2016)A Survey on Evolutionary Computation Approaches to Feature SelectionIEEE Transactions on Evolutionary Computation10.1109/TEVC.2015.250442020:4(606-626)Online publication date: Aug-2016
https://doi.org/10.1109/TEVC.2015.2504420
de Alcantara MMoreira TPedrini HFlórez-Revuelta F(2016)Action identification using a descriptor with autonomous fragments in a multilevel prediction schemeSignal, Image and Video Processing10.1007/s11760-016-0940-311:2(325-332)Online publication date: 19-Jul-2016
https://doi.org/10.1007/s11760-016-0940-3
Nguyen HXue BLiu IAndreae PZhang M(2016)New mechanism for archive maintenance in PSO-based multi-objective feature selectionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2128-820:10(3927-3946)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s00500-016-2128-8
Chaaraoui AFlórez-Revuelta F(2015)Continuous Human Action Recognition in Ambient Assisted Living ScenariosMobile Networks and Management10.1007/978-3-319-16292-8_25(344-357)Online publication date: 28-Feb-2015
https://doi.org/10.1007/978-3-319-16292-8_25
Chaaraoui APadilla-López JFerrández-Pastor FNieto-Hidalgo MFlórez-Revuelta F(2014)A Vision-Based System for Intelligent Monitoring: Human Behaviour Analysis and Privacy by ContextSensors10.3390/s14050889514:5(8895-8925)Online publication date: 20-May-2014
https://doi.org/10.3390/s140508895
Alcantara MMoreira TPedrini H(2014)Real-time action recognition based on cumulative Motion shapes2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2014.6854134(2917-2921)Online publication date: May-2014
https://doi.org/10.1109/ICASSP.2014.6854134
Chaaraoui APadilla-López JFlórez-Revuelta F(2013)Fusion of Skeletal and Silhouette-Based Features for Human Action Recognition with RGB-D DevicesProceedings of the 2013 IEEE International Conference on Computer Vision Workshops10.1109/ICCVW.2013.19(91-97)Online publication date: 2-Dec-2013
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