The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Learning and recognizing human dynamics in video sequences
Abstract
This paper describes a probabilistic decomposition of human dynamics at multiple abstractions, and shows how to propagate hypotheses across space, time, and abstraction levels. Recognition in this framework is the succession of very general low level grouping mechanisms to increased specific and learned model based grouping techniques at higher levels. Hard decision thresholds are delayed and resolved by higher level statistical models and temporal context. Low-level primitives are areas of coherent motion found by EM clustering, mid-level categories are simple movements represented by dynamical systems, and high-level complex gestures are represented by Hidden Markov Models as successive phases of ample movements. We show how such a representation can be learned from training data, and apply It to the example of human gait recognition.
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Recognizing human action in time-sequential images using hidden Markov model
Yamato, J., Ohya, J., & Ishii, K. (2003) · Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1992.223161 · Cited by 1,273
Cited by
Efficient region tracking with parametric models of geometry and illumination
Hager, G. D., & Belhumeur, P. N. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.722606 · Cited by 1,066
Modeling and Prediction of Human Behavior
Pentland, A., & Liu, A. (1999) · Neural Computation · doi:10.1162/089976699300016890 · Cited by 520
A survey on behavior analysis in video surveillance for homeland security applications
Ko, T. (2008) · 2008 37th IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2008.4906450 · Cited by 209
A computational model of event segmentation from perceptual prediction
Reynolds, J. R., Zacks, J. M., & Braver, T. S. (2007) · Cognitive Science · doi:10.1080/15326900701399913 · Cited by 172
Duric, Z., Gray, W. D., Heishman, R., Li, F., Rosenfeld, A., Schoelles, M. J., Schunn, C., & Wechsler, H. (2002) · Proceedings of the IEEE · doi:10.1109/jproc.2002.801449 · Cited by 185
Open-source tools for behavioral video analysis: Setup, methods, and best practices
Luxem, K., Sun, J. J., Bradley, S. P., Krishnan, K., Yttri, E., Zimmermann, J., Pereira, T. D., & Laubach, M. (2023) · eLife · doi:10.7554/elife.79305 · Cited by 98
Associated Spatio-Temporal Capsule Network for Gait Recognition
Zhao, A., Dong, J., Li, J., Qi, L., & Zhou, H. (2021) · arXiv · doi:10.48550/arxiv.2101.02458 · Cited by 55
Learning and inference in parametric switching linear dynamic systems
Oh, S. M., Rehg, J. M., Balch, T., & Dellaert, F. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.135 · Cited by 40
Parameterized Duration Mmodeling for Switching Linear Dynamic Systems
Oh, S. M., Rehg, J. M., & Dellaert, F. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.218 · Cited by 26
Metadata
- Authors
- Bregler, C.
- Venue
- Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-22
- Pages
- 568-574
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2118836531
- Cited by
- 621
- References
- 2
- FWCI
- 41.63
- Type
- conference paper
- 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
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.
