Learning mixed-state Markov models for statistical motion texture tracking

A motion texture is the instantaneous scalar map of apparent motion values extracted from a dynamic or temporal texture. It is mostly displayed by natural scene elements (fire, smoke, water) but also involves more general textured motion patterns (eg. a crowd of people, a flock). In this work we are...

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Bibliographic Details
Published in2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops pp. 444 - 451
Main Authors Crivelli, T, Bouthemy, P, Cernuschi-Frias, B, Yao, J.-F
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2009
Subjects
Conferences
Displays
Fires
Histograms
Layout
Legged locomotion
Motion analysis
Motion estimation
Rivers
Tracking
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Summary:A motion texture is the instantaneous scalar map of apparent motion values extracted from a dynamic or temporal texture. It is mostly displayed by natural scene elements (fire, smoke, water) but also involves more general textured motion patterns (eg. a crowd of people, a flock). In this work we are interested in the modeling and tracking of motion textures. Experimentally we observe that such motion maps exhibit values of a mixed type: a discrete component at zero and a continuous component of non-null motion values. Thus, we propose a statistical characterization of motion textures based on a mixed-state causal modeling. Next, the problem of tracking is considered. A set of mixed-state model parameters is learned as a descriptive feature of the motion texture to track and displacement estimation is solved using the conditional Kullback-Leibler divergence for statistical window matching. Results and comparisons are presented on real sequences.
ISBN:142444442X
9781424444427
DOI:10.1109/ICCVW.2009.5457666