The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Recognizing 50 human action categories of web videos
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
Cited by
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
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
A Comprehensive Survey of Vision-Based Human Action Recognition Methods
Zhang, H.-B., Zhang, Y.-X., Zhong, B., Lei, Q., Yang, L., Du, J.-X., & Chen, D.-S. (2019) · Sensors · doi:10.3390/s19051005 · Cited by 503
“Hollywood2 [97] √ × × 1707 12 HMDB51 [98] √ × × 6766 51 Olympic Sports [99] √ × × 783 16 UCF50 [100] √ × × 6618 50 UCF101 [101] √ × × 13,320 101 Kinetics [102] √ × × 306,245 400 MSR-Action3D [103] × √ √ 567 20 MSR-Daily Activity [104] √ √ √ 320 16 Northwestern-UCLA [105] √ √ √ 1475 10 UTD-MHAD [106] √ √ √ 861 27 RGBD-…”
A review of Convolutional-Neural-Network-based action recognition
Yao, G., Lei, T., & Zhong, J. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.05.018 · Cited by 437
A Review on Human Activity Recognition Using Vision-Based Method
Zhang, S., Wei, Z., Nie, J., Huang, L., Wang, S., & Li, Z. (2017) · Journal of Healthcare Engineering · doi:10.1155/2017/3090343 · Cited by 328
Metadata
- Authors
- Reddy, K. K., & Shah, M.
- Venue
- Machine Vision and Applications
- Published
- 2012-11-16
- Volume
- 24
- Issue
- 5
- Pages
- 971-981
- ISSN
- 0932-8092 (ISSN-L)
- 1432-1769
- OpenAlex
- W1981781955
- Cited by
- 655
- References
- 1
- FWCI
- 56.39
- Type
- journal article
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Recognizing 50 human action categories of web videos. https://app.cochat.ai/papers/10.1007/s00138-012-0450-4
- The paper
- Reddy, K. K., & Shah, M. (2013). Recognizing 50 human action categories of web videos. Machine Vision and Applications. https://doi.org/10.1007/s00138-012-0450-4
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.