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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

96 Citations (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.

Original language	English
Title of host publication	Computer Vision - ECCV 2014 Workshops, Proceedings
Editors	Michael M. Bronstein, Carsten Rother, Lourdes Agapito
Number of pages	15
Publisher	Springer
Publication date	2015
Pages	459-473
ISBN (Electronic)	9783319161778
DOIs	https://doi.org/10.1007/978-3-319-16178-5_32
Publication status	Published - 2015
Externally published	Yes
Event	13th European Conference on Computer Vision, ECCV 2014 - Zurich, Switzerland Duration: 6 Sept 2014 → 12 Sept 2014

Conference

Conference	13th European Conference on Computer Vision, ECCV 2014
Country/Territory	Switzerland
City	Zurich
Period	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

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8925
ISSN	0302-9743

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

Keywords

Action and interactions
Behavior analysis
Human pose recovery
Multi-modal gestures
Recognition

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

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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. In M. M. Bronstein, C. Rother, & L. Agapito (Eds.), Computer Vision - ECCV 2014 Workshops, Proceedings (pp. 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. editor / Michael M. Bronstein ; Carsten Rother ; Lourdes Agapito. Springer, 2015. pp. 459-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 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)",

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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. in MM Bronstein, C Rother & L Agapito (eds), Computer Vision - ECCV 2014 Workshops, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8925, pp. 459-473, 13th European Conference on Computer Vision, ECCV 2014, Zurich, Switzerland, 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. ed. / Michael M. Bronstein; Carsten Rother; Lourdes Agapito. Springer, 2015. p. 459-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8925).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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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

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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.

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KW - Behavior analysis

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

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

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BT - Computer Vision - ECCV 2014 Workshops, Proceedings

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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. In Bronstein MM, Rother C, Agapito L, editors, Computer Vision - ECCV 2014 Workshops, Proceedings. Springer. 2015. p. 459-473. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8925). doi: 10.1007/978-3-319-16178-5_32

Chalearn looking at people challenge 2014: Dataset and results

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Keywords

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