The DOI matches its Crossref record, with a small difference in title or year; 17 of the 18 references it cites were found in a registry or an index.
ChaLearn Looking at People Challenge 2014: Dataset and Results
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The Chalearn 2014 Looking at People Challenge (track 3) dataset [14] consists of 13,858 instances of Italian conversational gestures performed by different people and recorded with a consumer RGB-D sensor.”
From ModDrop: Adaptive Multi-Modal Gesture Recognition (2016) · cites this paper as [14]
Citations
Cites
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Learning realistic human actions from movies
Ivan Laptev, A., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587756 · Cited by 3,481
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Recognizing human actions: A local SVM approach
Schüldt, C., Laptev, I., & Barbara Caputo, B. (2004) · Cited by 2,908
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.471 · Cited by 2,699
Aggregating Local Image Descriptors into Compact Codes
Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., & Schmid, C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.235 · Cited by 1,503
Poselets: Body part detectors trained using 3D human pose annotations
Bourdev, L., & Malik, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459303 · Cited by 961
Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation
Johnson, S., & Everingham, M. (2010) · British Machine Vision Conference (BMVC) · doi:10.5244/c.24.12 · Cited by 870
Articulated Human Detection with Flexible Mixtures of Parts
Yang, Y., & Ramanan, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.261 · Cited by 843
Progressive search space reduction for human pose estimation
Ferrari, V., Marín-Jiménez, M. J., & Zisserman, A. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587468 · Cited by 613
Cited by
Sorted by citations
Items 201–212 of 212
Abuchi, O. A., & Ramesh, M. R. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.62046
Mira, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-62814-6_13
Sign Languages as Source Language for Machine Translation: Historical Overview and Challenges
Dambre, J., & Coster, M. D. (2024) · Machine translation · doi:10.1007/978-3-031-47362-3_7
Signease: Machine Learning Based Sign Language Interpreter
Parwez, L., Abrar, H., & Ali, M. A. M. (2025) · International Conference on Engineering & Computing Technologies (ICECT) · doi:10.1109/icect66235.2025.11381681
Deep learning for human motion analysis
Neverova, N. (2016) · theses.fr (ABES) · doi:10.70675/a8cda552zdc96z48c1z979bzac140403e58f · Cited by 13
Dynamic hand gesture recognition : from traditional handcrafted to recent deep learning approaches
de Smedt, Q. (2017) · theses.fr (ABES) · doi:10.70675/68ddb06cz1c43z41c3z828fzc58326157b3c
Apprentissage neuronal profond pour l'analyse de contenus multimodaux et temporels
Vielzeuf, V. (2019) · theses.fr (ABES) · doi:10.70675/cd4ec871zb372z492fz94bfz25086c5fda4a
Vision-based human gestures recognition for human-robot interaction
Mazhar, O. (2019) · theses.fr (ABES) · doi:10.70675/3d2ca177zb310z4cddz828fz42baea348aef
Granger, N. (2019) · theses.fr (ABES) · doi:10.70675/9f7530bez3094z498fzbaf7zbfac62e25c00
Reconnaissance d'actions à l'aide d'un imageur infrarouge basse résolution
de Ndjampa, F.-B. P. (2020) · theses.fr (ABES) · doi:10.70675/fc92054dzf7a0z4912z9a51z56ee96a1b53f
DexAvatar: 3D Sign Language Reconstruction with Hand and Body Pose Priors
Kundu, K., Barua, H. B., Robertson-Bell, L., Cai, Z., & Stefanov, K. (2026) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61042.2026.00566
Metadata
- Authors
- Escalera, S., Baró, X., Gonzàlez, J., Bautista, M. A., Madadi, M., Reyes, M., Ponce-López, V., Escalante, H. J., Shotton, J., & Guyon, I.
- Affiliations
- Universitat Oberta de Catalunya (ES)
- Universitat Autònoma de Barcelona (ES)
- Computer Vision Center (ES)
- National Institute of Astrophysics, Optics and Electronics (MX)
- Microsoft (United States) (US)
- ChaLearn (US)
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 459-473
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W619311409
- Cited by
- 211
- References
- 18
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 11 · 2023: 14 · 2022: 11 · 2021: 19 · 2020: 17 · 2019: 14 · 2018: 27 · 2017: 28 · 2016: 24 · 2015: 37
- FWCI
- 15.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- human action recognition, user-independent recognition, Jaccard similarity, benchmark dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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