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.
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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
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
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