The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning to Parse Pictures of People
Open accessOverview
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
The Representation and Matching of Pictorial Structures
Fischler, M. A., & Elschlager, R. A. (1973) · IEEE Transactions on Computers · doi:10.1109/t-c.1973.223602 · Cited by 1,322
Research works printed but not linked: 24
- C. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995. · DOI · Paper page
- E. Candes and D. Donoho. Curvelets-a surprisingly effective nonadaptive representation for objects with edges. In L. L. Schumaker et al., editor, Curves and Surfaces. Vanderbilt University Press, 1999.
- J. Coughlan, D. Snow, C. English, and A. Yuille. Efficient optimization of a deformable template using dynamic programming. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
- N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and other Kernel Based Learning Methods. Cambridge University Press, 2000. · DOI · Paper page
- M. Do and M. Vetterli. Orthonormal finite ridgelet transform for image compression. In Int. Conference on Image Processing, volume 2, pages 367–370, 2000.
- Pedro F. Felzenszwalb and Daniel P. Huttenlocher. Efficient matching of pictorial structures. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000.
- D. Forsyth and M. Fleck. Body plans. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997.
- B. Heisele, T. Poggio, and M. Pontil. Face detection in still gray images. Technical report, AI Memo 1687, Massachusetts Institute of Technology, 2000. · DOI · Paper page
- D. Hogg. Model-based vision: A program to see a walking person. Image and Vision Computing, 1(1):5–20, 1983. · DOI · Paper page
- Sergey Ioffe and David Forsyth. Human tracking with mixtures of trees. In Proc. Int. Conf. Computer Vision, 2001.
- Sergey Ioffe and David Forsyth. Mixtures of trees for object recognition. In Proc. IEEEConf. Computer Vision and Pattern Recognition, 2001.
- Sergey Ioffe and David Forsyth. Probabilistic methods for finding people. Int. J. of Computer Vision, 43(1), 2001. · DOI · Paper page
- S. Ju, M. Black, and Y. Yacoob. Cardboard people: a parameterized model of articulated image motion. In Int. Conference on Automatic Face and Gesture Recognition, 1996.
- D. Marr and H.K. Nishihara. Representation and recognition of the spatial organization of three dimensional structure. Proceedings of the Royal Society of London B, 200:269–294, 1978. · DOI · Paper page
- Anuj Mohan, Constantine Papageorgiou, and Tomaso Poggio. Example-based object detection in images by components. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(4), 2001. · DOI · Paper page
- D. Morris and J. Rehg. Singularity analysis for articulated object tracking. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1998.
- C. Papageorgiou. Object and pattern detection in video sequences. Technical report, Master’s thesis, Massachusetts Institute of Technology, 1997.
- K. Rohr. Incremental recognition of pedestrians from image sequences. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 8–13, 1993. · DOI
- H. Sidenbladh and M. Black. Learning the statistics of people in images and video. Int. Journal of Computer Vision, 2001.
- H. Sidenbladh, F. Torre, and M. Black. A framework for modeling the appearance of 3d articulated figures. In Int. Conference on Automatic Face and Gesture Recognition, 2000.
- M. Tipping. The relevance vector machine. In Advances in Neural Information Processing Systems. Morgan Kaufmann, 2000.
- M. E. Tipping. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211–244, 2001.
- V. Vapnik. Statistical Learning Theory. Wiley, 1998.
- Liang Zhao and Chuck Thorpe. Recursive context reasoning for human detection and parts identification. In IEEE Workshop on Human Modeling, Analysis and Synthesis, 2000.
Cited by
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Self-citationVision-based human motion analysis: An overview
Poppe, R. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.016 · Cited by 835
Human motion tracking for rehabilitation-A survey
Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813
Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images
Dantone, M., Gall, J., Leistner, C., & Van Gool, L. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2318702 · Cited by 73
ABYOLOv4: improved YOLOv4 human object detection based on enhanced multi-scale feature fusion
Li, R., Zeng, X., Yang, S., Li, Q., Yan, A., & Li, D. (2024) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-023-01105-z · Cited by 15
Metadata
- Authors
- Ronfard, R., Schmid, C., & Triggs, B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 700-714
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2168108030
- Cited by
- 211
- References
- 1
- FWCI
- 5.43
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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: Learning to Parse Pictures of People. https://app.cochat.ai/papers/10.1007/3-540-47979-1_47
- The paper
- Ronfard, R., Schmid, C., & Triggs, B. (2002). Learning to Parse Pictures of People. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-47979-1_47
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.