The Bayesian Occupation Filter (BOF) has proven successful for target tracking in the context of automotive applications. This paper describes an improved BOF for target tracking with lower computational costs while retaining the key advantages of the original BOF formulation. The BOF takes the form of a grid based decomposition of the environment. Sensory data provides information on the probability of occupancy for each cell of the BOF grid. In contrast to the original BOF, each cell of the newly proposed BOF contains a distribution over the velocity of the propagating cell occupancy. The distribution of the velocity for each cell occupancy can be estimated using a Bayesian filtering mechanism. An inevitable problem when using a grid space representation especially in dynamic environments is discretization. A method is proposed in this paper to deal with the discretization problem. Object based representations does not exist in the BOF grids. How- ever, there are often applications which requires the definition and track- ing at the object level. A general grid based clustering and standard target tracking methodology can be applied to obtain this object level representation. To demonstrate the generality and robustness of the clustering tracking methodology when applied to the BOF framework, experiments based on tracking humans in indoor environment were conducted. The Joint Probabilistic Data Association (JPDA) algorithm has been applied to publicly available data from the European Project CAVIAR, taken from an indoor shopping center.