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 grids. Different from conventional occupancy gridmaps, in BOF, each grid has both static (occupancy) and dynamic (velocity) characteristics. In the new proposed BOF, the velocity of each cell is modeled as a distribution. The distribution for each cell occupancy can therefore be inferred using a filtering mechanism. A segmentation algorithm is implemented to extract the objects from BOF estimation. Thereafter, standard target tracking methods are employed to further analyze each object's motion. By using BOF as a pre-processing tool, the complexity of the data association is significantly reduced. Experiments using data from an indoor human tracking application demonstrate that our approach yields satisfactory results
['Cheng Chen', 'Christopher Tay', 'Christian Laugier', 'Kamel Mekhnacha']
Dynamic Environment Modeling with Gridmap: A Multiple-Object Tracking Application