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Research Team



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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.