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In many computer vision related applications it is necessary to distinguish between the background of an image and the objects that are contained in it. This is a difficult problem because of the constraints imposed by the available time and the computational cost of robust object extraction algorithms. This report describes a new method that benefits from state of the art background/foreground classification...
The Bayesian occupancy filter (BOF) (Coue et al., 2002) has achieved promising results in the object tracking applications. This paper presents a new development of BOF which inherits original BOF's advantages. Meanwhile, the new formulation has significantly reduced original BOF's complexities and can be run in realtime. In Bayesian occupancy filter, the environment is finely divided into 2-dimensional...
Despite their popularity, occupancy grids cannot be directly applied to problems where the identity of the objects populating an environment needs to be taken into account (e.g., object tracking, scene interpretation, etc.), in this cases it is necessary to postprocess the grid in order to extract object information. This paper approaches the problem by proposing a novel algorithm inspired on image...
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