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Title: Data association for multi target-multi model particle filtering: implicit assignment to weighted assignment
Authors: ZAVERI, MA
Keywords: Gaussian Processes
Markov Processes
Filtering Theory
State-Space Methods
Target Tracking
Issue Date: 2004
Publisher: IEEE
Citation: Proceedings of the International Conference on Signal Processing and Communications, Bangalore, India, 11-14 December 2004, 27-31
Abstract: In multiple target tracking the data association, i.e. observation to track assignment, and the model selection to track arbitrary tra-jectory 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-lineadnon-Gaussian state space model for target tracking. Data association problem is solved using (a) an implicit observation, (h) a centroid of observations (c) Markov random field (MRF) for observation to track assignment.
ISBN: 0-7803-8674-4
Appears in Collections:Proceedings papers

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