Chalearn looking at people challenge 2014: Dataset and results

Sergio Escalera; Xavier Baró; Jordi Gonzàlez; Miguel A. Bautista; Meysam Madadi; Miguel Reyes; Víctor Ponce-López; Hugo J. Escalante; Jamie Shotton; Isabelle Guyon

doi:10.1007/978-3-319-16178-5_32

Chalearn looking at people challenge 2014: Dataset and results

Sergio Escalera, Xavier Baró, Jordi Gonzàlez, Miguel A. Bautista, Meysam Madadi, Miguel Reyes, Víctor Ponce-López, Hugo J. Escalante, Jamie Shotton, Isabelle Guyon

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

96 Citationer (Scopus)

Abstract

This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively.

Originalsprog	Engelsk
Titel	Computer Vision - ECCV 2014 Workshops, Proceedings
Redaktører	Michael M. Bronstein, Carsten Rother, Lourdes Agapito
Antal sider	15
Forlag	Springer
Publikationsdato	2015
Sider	459-473
ISBN (Elektronisk)	9783319161778
DOI	https://doi.org/10.1007/978-3-319-16178-5_32
Status	Udgivet - 2015
Udgivet eksternt	Ja
Begivenhed	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Schweiz Varighed: 6 sep. 2014 → 12 sep. 2014

Konference

Konference	13th European Conference on Computer Vision, ECCV 2014
Land/Område	Schweiz
By	Zurich
Periode	06/09/2014 → 12/09/2014
Sponsor	Adobe, Amazon, Ascending Technologies, Bosch, Disney Research, Facebook, Google, IJAC, iniLabs, Leica, M-tec, Microsoft Research, Mitsubishi Electric, mOBILEYE, NICTA, NVIDIA, OMRON Corporation, Pelican imaging, Qualcomm, Siemens, SUPERFISH, technicolor, University of Adelaide, The University of North Carolina, Toshiba Corporation, Toyota, VIZrt

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	8925
ISSN	0302-9743

Bibliografisk note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

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10.1007/978-3-319-16178-5_32

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Citationsformater

Escalera, S., Baró, X., Gonzàlez, J., Bautista, M. A., Madadi, M., Reyes, M., Ponce-López, V., Escalante, H. J., Shotton, J., & Guyon, I. (2015). Chalearn looking at people challenge 2014: Dataset and results. I M. M. Bronstein, C. Rother, & L. Agapito (red.), Computer Vision - ECCV 2014 Workshops, Proceedings (s. 459-473). Springer. https://doi.org/10.1007/978-3-319-16178-5_32

Escalera, Sergio ; Baró, Xavier ; Gonzàlez, Jordi et al. / Chalearn looking at people challenge 2014 : Dataset and results. Computer Vision - ECCV 2014 Workshops, Proceedings. red. / Michael M. Bronstein ; Carsten Rother ; Lourdes Agapito. Springer, 2015. s. 459-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 8925).

@inproceedings{09963b33d13249fea6366e32189e2ca8,

title = "Chalearn looking at people challenge 2014: Dataset and results",

abstract = "This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively.",

keywords = "Action and interactions, Behavior analysis, Human pose recovery, Multi-modal gestures, Recognition",

author = "Sergio Escalera and Xavier Bar{\'o} and Jordi Gonz{\`a}lez and Bautista, {Miguel A.} and Meysam Madadi and Miguel Reyes and V{\'i}ctor Ponce-L{\'o}pez and Escalante, {Hugo J.} and Jamie Shotton and Isabelle Guyon",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",

year = "2015",

doi = "10.1007/978-3-319-16178-5_32",

language = "English",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "459--473",

editor = "Bronstein, {Michael M.} and Carsten Rother and Lourdes Agapito",

booktitle = "Computer Vision - ECCV 2014 Workshops, Proceedings",

address = "Germany",

}

Escalera, S, Baró, X, Gonzàlez, J, Bautista, MA, Madadi, M, Reyes, M, Ponce-López, V, Escalante, HJ, Shotton, J & Guyon, I 2015, Chalearn looking at people challenge 2014: Dataset and results. i MM Bronstein, C Rother & L Agapito (red), Computer Vision - ECCV 2014 Workshops, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 8925, s. 459-473, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Schweiz, 06/09/2014. https://doi.org/10.1007/978-3-319-16178-5_32

Chalearn looking at people challenge 2014: Dataset and results. / Escalera, Sergio; Baró, Xavier; Gonzàlez, Jordi et al.
Computer Vision - ECCV 2014 Workshops, Proceedings. red. / Michael M. Bronstein; Carsten Rother; Lourdes Agapito. Springer, 2015. s. 459-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 8925).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Chalearn looking at people challenge 2014

T2 - 13th European Conference on Computer Vision, ECCV 2014

AU - Escalera, Sergio

AU - Baró, Xavier

AU - Gonzàlez, Jordi

AU - Bautista, Miguel A.

AU - Madadi, Meysam

AU - Reyes, Miguel

AU - Ponce-López, Víctor

AU - Escalante, Hugo J.

AU - Shotton, Jamie

AU - Guyon, Isabelle

PY - 2015

Y1 - 2015

N2 - This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively.

AB - This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively.

KW - Action and interactions

KW - Behavior analysis

KW - Human pose recovery

KW - Multi-modal gestures

KW - Recognition

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DO - 10.1007/978-3-319-16178-5_32

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AN - SCOPUS:84925310875

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 473

BT - Computer Vision - ECCV 2014 Workshops, Proceedings

A2 - Bronstein, Michael M.

A2 - Rother, Carsten

A2 - Agapito, Lourdes

PB - Springer

Y2 - 6 September 2014 through 12 September 2014

ER -

Escalera S, Baró X, Gonzàlez J, Bautista MA, Madadi M, Reyes M et al. Chalearn looking at people challenge 2014: Dataset and results. I Bronstein MM, Rother C, Agapito L, red., Computer Vision - ECCV 2014 Workshops, Proceedings. Springer. 2015. s. 459-473. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 8925). doi: 10.1007/978-3-319-16178-5_32

Chalearn looking at people challenge 2014: Dataset and results

Abstract

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