Relevant kinematic feature selection to support human action recognition in MoCap data

This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly,...

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Autores:
Pulgarín Giraldo, Juan Diego
Alvarez Meza, Andrés Marino
Ruales Torres, Anderson Alberto
Castellanos-Dominguez, German
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Autónoma de Occidente
Repositorio:
RED: Repositorio Educativo Digital UAO
Idioma:
eng
OAI Identifier:
oai:red.uao.edu.co:10614/11187
Acceso en línea:
http://hdl.handle.net/10614/11187
https://doi.org/10.1007/978-3-319-59773-7_51
Palabra clave:
Cinemática
Kinematics
Movimientos mecánicos
Mechanical movements
Center kernel alignment
Feature selection
Human motion
Kinematics
Motion capture data
Principal component analysis
Relevance
ReliefF
Rights
openAccess
License
Derechos Reservados - Universidad Autónoma de Occidente
id REPOUAO2_b13b5d86fbd1b2b802fa4e0b54b7fcaf
oai_identifier_str oai:red.uao.edu.co:10614/11187
network_acronym_str REPOUAO2
network_name_str RED: Repositorio Educativo Digital UAO
repository_id_str
dc.title.eng.fl_str_mv Relevant kinematic feature selection to support human action recognition in MoCap data
title Relevant kinematic feature selection to support human action recognition in MoCap data
spellingShingle Relevant kinematic feature selection to support human action recognition in MoCap data
Cinemática
Kinematics
Movimientos mecánicos
Mechanical movements
Center kernel alignment
Feature selection
Human motion
Kinematics
Motion capture data
Principal component analysis
Relevance
ReliefF
title_short Relevant kinematic feature selection to support human action recognition in MoCap data
title_full Relevant kinematic feature selection to support human action recognition in MoCap data
title_fullStr Relevant kinematic feature selection to support human action recognition in MoCap data
title_full_unstemmed Relevant kinematic feature selection to support human action recognition in MoCap data
title_sort Relevant kinematic feature selection to support human action recognition in MoCap data
dc.creator.fl_str_mv Pulgarín Giraldo, Juan Diego
Alvarez Meza, Andrés Marino
Ruales Torres, Anderson Alberto
Castellanos-Dominguez, German
dc.contributor.author.none.fl_str_mv Pulgarín Giraldo, Juan Diego
Alvarez Meza, Andrés Marino
Ruales Torres, Anderson Alberto
Castellanos-Dominguez, German
dc.subject.lemb.spa.fl_str_mv Cinemática
topic Cinemática
Kinematics
Movimientos mecánicos
Mechanical movements
Center kernel alignment
Feature selection
Human motion
Kinematics
Motion capture data
Principal component analysis
Relevance
ReliefF
dc.subject.lemb.eng.fl_str_mv Kinematics
dc.subject.armarc.spa.fl_str_mv Movimientos mecánicos
dc.subject.armarc.eng.fl_str_mv Mechanical movements
dc.subject.proposal.eng.fl_str_mv Center kernel alignment
Feature selection
Human motion
Kinematics
Motion capture data
Principal component analysis
Relevance
ReliefF
description This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly, the method with the best results includes the supervised information regarding the action to find out a relevant set of features, encoding the most discriminative information. Experimental results are obtained on a well-known public data (MSR Action3D). Results are encouraging to use kernel theory methods to get better kinematic feature selection for each action with a good generalization indistinct to the subject
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2019-10-09T21:15:39Z
dc.date.available.none.fl_str_mv 2019-10-09T21:15:39Z
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
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status_str publishedVersion
dc.identifier.isbn.spa.fl_str_mv 9783319597720 (impreso)
9783319597737 (en línea)
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10614/11187
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-3-319-59773-7_51
identifier_str_mv 9783319597720 (impreso)
9783319597737 (en línea)
url http://hdl.handle.net/10614/11187
https://doi.org/10.1007/978-3-319-59773-7_51
dc.language.iso.eng.fl_str_mv eng
language eng
dc.relation.eng.fl_str_mv Biomedical Applications Based on Natural and Artificial Computing : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part II. Páginas 501-509, (2017)
dc.relation.haspart.eng.fl_str_mv Biomedical Applications Based on Natural and Artificial Computing International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017 Corunna, Spain, June 19–23, 2017 Proceedings, Part II
dc.relation.cites.spa.fl_str_mv Pulgarin-Giraldo, J. D., Ruales-Torres, A. A., Álvarez-Meza, A. M., & Castellanos-Dominguez, G. (2017, June). Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(10338, 501-509). Springer, Cham.DOI: 10.1007/978-3-319-59773-7 51
dc.relation.ispartofbook.eng.fl_str_mv Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science
dc.relation.references.none.fl_str_mv 1. Kale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. IJACI 7(2), 75–92 (2016)
2. Mandery, C., Plappert, M., Sol, J.B., Asfour, T.: Dimensionality reduction for whole-body human motion recognition. In: 19th International Conference on Information Fusion, FUSION, Heidelberg, Germany, 5–8 July 2016, pp. 355–362 (2016)
3. Althloothi, S., Mahoor, M.H., Zhang, X., Voyles, R.M.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recogn. 47(5), 1800–1812 (2014)
4. Li, M., Leung, H., Liu, Z., Zhou, L.: 3D human motion retrieval using graph kernels based on adaptive graph construction. Comput. Graph. Pergamon 54, 104–112 (2016)
5. Wang, L., Zhao, G., Cheng, L., Pietikainen, M.: Machine Learning for Vision-Based Motion Analysis, 1st edn. Springer, Heidelberg (2011)
6. Wei, X.K., Chai, J.: Modeling 3D human poses from uncalibrated monocular images. In: IEEE 12th International Conference on Computer Vision, ICCV, Kyoto, Japan, 27 September–4 October 2009, pp. 1873–1880 (2009)
7. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshopps 2010, San Francisco, CA, USA, 13–18 June 2010, pp. 9–14 (2010)
8. Ravet, T., Tilmanne, J., d’Alessandro, N.: Hidden Markov model based real-time motion recognition and following. In: Proceedings of the 2014 International Workshop on Movement, Computing, MOCO 2014, pp. 82–87. ACM, New York (2014)
9. Jiang, Y., Saxena, A.: Modeling high-dimensional humans for activity anticipation using Gaussian process latent CRFs. In: Robotics: Science and Systems X, University of California, Berkeley, USA, 12–16 July 2014 (2014)
10. Garc´ıa-Vega, S., ´Alvarez-Meza, A.M., Castellanos-Dom´ınguez, C.G.: MoCap data segmentation and classification using kernel based multi-channel analysis. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 495–502. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41827-3 62
11. Pulgarin-Giraldo, J.D., Alvarez-Meza, A.M., Melo-Betancourt, L.G., Ramos-Bermudez, S., Castellanos-Dominguez, G.: A similarity indicator for differentiating kinematic performance between qualified tennis players. In: Beltr´an-Casta˜n´on, C., Nystr¨om, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 309–317. Springer, Cham (2017). doi:10.1007/978-3-319-52277-7 38
12. Diaz-Martinez, N.F., Pulgarin-Giraldo, J.D., Vinasco-Isaza, L.E., Agredo, W.: Analysis of the alignment angles and flexion angle in women with patellofemoral pain syndrome. In: Torres, I., Bustamante, J., Sierra, D. (eds.) CLAIB 2016. IFMBE Proceedings. Springer, Singapore (2016)
13. Yang, X., Tian, Y.: Effective 3D action recognition using eigenjoints. J. Vis. Commun. Image Represent. 25(1), 2–11 (2014)
14. Yang, X., Tian, Y.: Eigenjoints-based action recognition using Na¨ıve-Bayes-Nearest-Neighbor. In: CVPR Workshops, pp. 14–19. IEEE Computer Society (2012)
15. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014)16. Alvarez-Meza, A.M., Velasquez-Martinez, L.F., Castellanos-Dominguez, G.: Timeseries discrimination using feature relevance analysis in motor imagery classification. Neurocomputing 151(Part 1), 122–129 (2015)
17. Brockmeier, A.J., Choi, J.S., Kriminger, E.G., Francis, J.T., Principe, J.C.: Neural decoding with kernel-based metric learning. Neural Comput. 26(6), 1080–1107 (2014)
18. Zeng, X.,Wang, Q., Zhang, C., Cai, H.: Feature selection based on reliefF and PCA for underwater sound classification. In: Proceedings of 3rd International Conference on Computer Science and Network Technology, pp. 442–445 (2013)
dc.rights.spa.fl_str_mv Derechos Reservados - Universidad Autónoma de Occidente
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spelling Pulgarín Giraldo, Juan Diegovirtual::4143-1Alvarez Meza, Andrés Marino5154abb266e67961a71f8dc28e883cbfRuales Torres, Anderson Albertoc4e9d8430ebbca3f3657cebc87184857Castellanos-Dominguez, Germane7877f60c5ac464594daa00d2d4e8180Universidad Autónoma de Occidente. Calle 25 115-85. Km 2 vía Cali-Jamundí2019-10-09T21:15:39Z2019-10-09T21:15:39Z20179783319597720 (impreso)9783319597737 (en línea)http://hdl.handle.net/10614/11187https://doi.org/10.1007/978-3-319-59773-7_51This paper presents a feature selection comparison oriented to human action recognition only with the kinematic features of skeleton representation. For this purpose, three relevance methods are used to rank the contribution of kinematic features for classifying an action is employed. Particularly, the method with the best results includes the supervised information regarding the action to find out a relevant set of features, encoding the most discriminative information. Experimental results are obtained on a well-known public data (MSR Action3D). Results are encouraging to use kernel theory methods to get better kinematic feature selection for each action with a good generalization indistinct to the subjectapplication/pdf9 páginasengSpringerBiomedical Applications Based on Natural and Artificial Computing : International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part II. Páginas 501-509, (2017)Biomedical Applications Based on Natural and Artificial Computing International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017 Corunna, Spain, June 19–23, 2017 Proceedings, Part IIPulgarin-Giraldo, J. D., Ruales-Torres, A. A., Álvarez-Meza, A. M., & Castellanos-Dominguez, G. (2017, June). Relevant Kinematic Feature Selection to Support Human Action Recognition in MoCap Data. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(10338, 501-509). Springer, Cham.DOI: 10.1007/978-3-319-59773-7 51Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science1. Kale, G.V., Patil, V.H.: A study of vision based human motion recognition and analysis. IJACI 7(2), 75–92 (2016)2. Mandery, C., Plappert, M., Sol, J.B., Asfour, T.: Dimensionality reduction for whole-body human motion recognition. In: 19th International Conference on Information Fusion, FUSION, Heidelberg, Germany, 5–8 July 2016, pp. 355–362 (2016)3. Althloothi, S., Mahoor, M.H., Zhang, X., Voyles, R.M.: Human activity recognition using multi-features and multiple kernel learning. Pattern Recogn. 47(5), 1800–1812 (2014)4. Li, M., Leung, H., Liu, Z., Zhou, L.: 3D human motion retrieval using graph kernels based on adaptive graph construction. Comput. Graph. Pergamon 54, 104–112 (2016)5. Wang, L., Zhao, G., Cheng, L., Pietikainen, M.: Machine Learning for Vision-Based Motion Analysis, 1st edn. Springer, Heidelberg (2011)6. Wei, X.K., Chai, J.: Modeling 3D human poses from uncalibrated monocular images. In: IEEE 12th International Conference on Computer Vision, ICCV, Kyoto, Japan, 27 September–4 October 2009, pp. 1873–1880 (2009)7. Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshopps 2010, San Francisco, CA, USA, 13–18 June 2010, pp. 9–14 (2010)8. Ravet, T., Tilmanne, J., d’Alessandro, N.: Hidden Markov model based real-time motion recognition and following. In: Proceedings of the 2014 International Workshop on Movement, Computing, MOCO 2014, pp. 82–87. ACM, New York (2014)9. Jiang, Y., Saxena, A.: Modeling high-dimensional humans for activity anticipation using Gaussian process latent CRFs. In: Robotics: Science and Systems X, University of California, Berkeley, USA, 12–16 July 2014 (2014)10. Garc´ıa-Vega, S., ´Alvarez-Meza, A.M., Castellanos-Dom´ınguez, C.G.: MoCap data segmentation and classification using kernel based multi-channel analysis. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8259, pp. 495–502. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41827-3 6211. Pulgarin-Giraldo, J.D., Alvarez-Meza, A.M., Melo-Betancourt, L.G., Ramos-Bermudez, S., Castellanos-Dominguez, G.: A similarity indicator for differentiating kinematic performance between qualified tennis players. In: Beltr´an-Casta˜n´on, C., Nystr¨om, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 309–317. Springer, Cham (2017). doi:10.1007/978-3-319-52277-7 3812. Diaz-Martinez, N.F., Pulgarin-Giraldo, J.D., Vinasco-Isaza, L.E., Agredo, W.: Analysis of the alignment angles and flexion angle in women with patellofemoral pain syndrome. In: Torres, I., Bustamante, J., Sierra, D. (eds.) CLAIB 2016. IFMBE Proceedings. Springer, Singapore (2016)13. Yang, X., Tian, Y.: Effective 3D action recognition using eigenjoints. J. Vis. Commun. Image Represent. 25(1), 2–11 (2014)14. Yang, X., Tian, Y.: Eigenjoints-based action recognition using Na¨ıve-Bayes-Nearest-Neighbor. In: CVPR Workshops, pp. 14–19. IEEE Computer Society (2012)15. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (SMIJ): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014)16. Alvarez-Meza, A.M., Velasquez-Martinez, L.F., Castellanos-Dominguez, G.: Timeseries discrimination using feature relevance analysis in motor imagery classification. Neurocomputing 151(Part 1), 122–129 (2015)17. Brockmeier, A.J., Choi, J.S., Kriminger, E.G., Francis, J.T., Principe, J.C.: Neural decoding with kernel-based metric learning. Neural Comput. 26(6), 1080–1107 (2014)18. Zeng, X.,Wang, Q., Zhang, C., Cai, H.: Feature selection based on reliefF and PCA for underwater sound classification. In: Proceedings of 3rd International Conference on Computer Science and Network Technology, pp. 442–445 (2013)Derechos Reservados - Universidad Autónoma de Occidentehttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2https://link.springer.com/content/pdf/10.1007%2F978-3-319-59773-7.pdfRelevant kinematic feature selection to support human action recognition in MoCap dataCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85CinemáticaKinematicsMovimientos mecánicosMechanical movementsCenter kernel alignmentFeature selectionHuman motionKinematicsMotion capture dataPrincipal component analysisRelevanceReliefFPublication33e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4143-133e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4143-1https://scholar.google.com.co/citations?user=Bwuc2BkAAAAJ&hl=envirtual::4143-10000-0002-6409-5104virtual::4143-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000207497virtual::4143-1CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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