An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments
Meng Keat Christopher Tay, Kamel Mekhnacha, Chia-Hsien Chen, Manuel Yguel, Christian Laugier · International Journal of Vehicle Autonomous Systems · 2008
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. In contrast to the original BOF, each cell of the newly proposed BOF contains an additional distribution over the velocity of the propagating cell occupancy. This is estimated using Bayesian filtering. We propose how to deal with the grid discretisation problem. Object-based representations do not exist in the BOF grids. However, there are often applications which require the definition and tracking at the object level. A simple clustering and target tracking methodology is used to illustrate how to obtain this object level representation. Experiments based on tracking humans in indoor environment were conducted.