Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,512

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Mining actionlet ensemble for action recognition with depth cameras

Open access
Jiang Wang, Zicheng Liu, Ying Wu, Junsong Yuan·2012·2012 IEEE Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2012.6247813
Open this paper in CoChat — free account

Abstract

Human action recognition is an important yet challenging task. The recently developed commodity depth sensors open up new possibilities of dealing with this problem but also present some unique challenges. The depth maps captured by the depth cameras are very noisy and the 3D positions of the tracked joints may be completely wrong if serious occlusions occur, which increases the intra-class variations in the actions. In this paper, an actionlet ensemble model is learnt to represent each action and to capture the intra-class variance. In addition, novel features that are suitable for depth data are proposed. They are robust to noise, invariant to translational and temporal misalignments, and capable of characterizing both the human motion and the human-object interactions. The proposed approach is evaluated on two challenging action recognition datasets captured by commodity depth cameras, and another dataset captured by a MoCap system. The experimental evaluations show that the proposed approach achieves superior performance to the state of the art algorithms.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cvpr.2012.6247813

Cites

3 of 3 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Research works printed but not linked: 21

  1. oppenheim, 1999, Discrete Time Signal Processing
  2. 10.1109/CVPR.2010.5540234 · DOI · Paper page
  3. muller, 2006, Motion templates for automatic classification and retrieval of motion capture data, Proceedings of the 2006 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 137
  4. 10.1109/CVPR.2011.5995476 · DOI
  5. ning, 2008, Latent pose estimator for continuous action, ECCV, 419
  6. zhu, 2008, Max margin AND/OR graph learning for parsing the human body, CVPR, 1
  7. lv, 2006, Recognition and segmentation of 3-D human action using HMM and multi-class AdaBoost, ECCV, 359
  8. martens, 2011, Learning recurrent neural networks with hessian-free optimization, ICML
  9. li, 2011, Time series clustering: Complex is simpler!, ICML
  10. 10.1109/CVPRW.2010.5543273 · DOI · Paper page
  11. 10.1109/CVPR.2008.4587756 · DOI
  12. wu, 2004, Probability estimates for multi-class classification by pairwise coupling, Journal of Machine Learning Research, 5, 975
  13. 10.1109/ICCV.1995.466880 · DOI · Paper page
  14. 10.1109/CVPR.2011.5995316 · DOI
  15. agrawal, 1994, Fast algorithms for mining association rules, VLDB, 1215, 487
  16. 0, CMU Graphics Lab Motion Capture Database
  17. 10.1007/s11263-005-1838-7 · DOI · Paper page
  18. 10.1109/CVPR.2007.383274 · DOI
  19. 10.1145/1178782.1178808 · DOI
  20. 10.1023/A:1012450327387 · DOI
  21. 10.1016/j.imavis.2009.08.003 · DOI

Cited by

See all 1,512

Metadata

Authors
Wang, J., Liu, Z., Wu, Y., & Yuan, J.
Venue
2012 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2012-06
Pages
1290-1297
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2012.6247813
OpenAlex
W2143267104
Cited by
1,512
References
3
FWCI
85.47
Type
conference paper
Language
en
Open access
green

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

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.