A Variational Bayes Association-based Multi-object Tracker under the Non-homogeneous Poisson Measurement Process
Runze Gan, Qing Li, Simon Godsill
Abstract
The non-homogeneous Poisson process (NHPP) has been widely used to model extended object measurements where one object can generate zero or several measurements; it also provides an elegant solution to the computationally demanding data association problem in multiple object tracking. This paper presents an association-based NHPP system, based on which we propose a variational Bayes association-based NHPP (VB-AbNHPP) tracker that can estimate online the object kinematics and the association variables in parallel. In particular, the VB-AbNHPP tracker can be easily extended to include online static parameter learning (e.g., measurement rates) based on a general coordinate ascent variational filtering framework developed here. The results show that the proposed VB-AbNHPP tracker is superior to other competing methods in terms of implementation efficiency and tracking accuracy.