The Bayesian occupancy filter (BOF) [1] has achieved promising results in the object tracking applications. This paper presents a new development of BOF which inherits original BOF's advantages in handling occlusion and representing objects' shape. 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. Like the original BOF, no representation of objects exists in the BOF gridmap. However, there are often applications which require the definition and tracking at the object level. In the post-processing, 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. Experiments using data from an indoor human tracking application demonstrate that our approach yields satisfactory results even when serious occlusions exist.