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Recently, we have proposed an insightful description for the motion of
objects in images. This description assumes that objects have favorite types
of motion regimes characterized by motion fields. Since the motion of
pedestrians and vehicles is unpredictable, to a certain extent, we assume
that a single motion field is not enough to represent a wide variety of
patterns and, therefore, adopt multiple motion fields. In order to increase
the model flexibility, each object is allowed to switch from one motion
regime to another, according to switching probabilities computed from the
video data.
In this project we will explore the use of sparse techniques to
improve the estimation of multiple motion fields as well as spacevarying switching matrix (stochastic matrix) that
describes the switching process associated to the movement of each target.
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