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dc.contributor.authorVidal, René
dc.contributor.authorChaudhry, Rizwan
dc.date.accessioned2009-04-14T18:52:25Z
dc.date.available2009-04-14T18:52:25Z
dc.date.issued2009
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/33295
dc.description.abstractOver the past few years, several papers have used Linear Dynamical Systems (LDS)s for modeling, registration, segmentation, and recognition of visual dynamical processes, such as human gaits, dynamic textures and lip articulations. The recognition framework involves identifying the parameters of the LDSs from features extracted from a training set of videos, using metrics on the space of dynamical systems to compare them, and combining these metrics with different classification methods. Usually, each paper makes an ad-hoc choice for every step, and tests the recognition framework on small data sets often involving only one application. We present a detailed evaluation of the LDS-based recognition pipeline; comparing identification methods, metrics, and classification techniques. We propose new metrics that have certain invariance properties and explore a number of variations to the existing metrics. We perform experimental evaluations on well-known data sets of human gaits, dynamic textures, and lip articulations and provide benchmark recognition results. We also analyze the robustness of the recognition pipeline with respect to changes in observation and experimental conditions. Overall, this work represents the most extensive to-date evaluation of the LDS-based recognition framework.en_US
dc.description.sponsorshipThis work was partially supported by startup funds from JHU, by grants ONR N00014-05-10836, NSF CAREER 0447739, NSF EHS-0509101, and by contract JHU APL-934652.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesDepartment of Computer Science, April 2009;Technial Report 09-01
dc.subjectClassificationen_US
dc.subjectKernels for time series dataen_US
dc.subjectLinear dynamical systemsen_US
dc.subjectDynamic texturesen_US
dc.subjectAction recognitionen_US
dc.titleRecognition of Visual Dynamical Processes: Theory, Kernels, and Experimental Evaluationen_US
dc.typeWorking Paperen_US


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