Data association for multi target-multi model particle filtering: implicit assignment to weighted assignment

Abstract

In multiple target tracking the data association, i.e. observation to track assignment and the model selection to track arbitrary trajectory play an important role for success of any tracking algorithm. In this paper we propose various methods for data association in the presence of multiple targets and dense clutter along with the tracking algorithm using multiple model based particle filtering. Particle filtering allows one to use non-linear/non-Gaussian state space model for target tracking. Data association problem is solved using (a) an implicit observation, (b) a centroid of observations (c) Markov random field (MRF) for observation to track assignment.