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Motion Recognition Using Nonparametric Image Motion Models Estimated from Temporal and Multiscale Cooccurrence Statistics
December 2003 (vol. 25 no. 12)
pp. 1619-1624
Abstract—A new approach for motion characterization in image sequences is presented. It relies on the probabilistic modeling of temporal and scale cooccurrence distributions of local motion-related measurements directly computed over image sequences. Temporal multiscale Gibbs models allow us to handle both spatial and temporal aspects of image motion content within a unified statistical framework. Since this modeling mainly involves the scalar product between cooccurrence values and Gibbs potentials, we can formulate and address several fundamental issues: model estimation according to the ML criterion (hence, model training and learning) and motion classification. We have conducted motion recognition experiments over a large set of real image sequences comprising various motion types such as temporal texture samples, human motion examples, and rigid motion situations.
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Index Terms:
Nonparametric motion analysis, motion recognition, multiscale analysis, Gibbs models, cooccurrences, ML criterion.
Citation:
R. Fablet, P. Bouthemy, "Motion Recognition Using Nonparametric Image Motion Models Estimated from Temporal and Multiscale Cooccurrence Statistics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1619-1624, Dec. 2003, doi:10.1109/TPAMI.2003.1251155
