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A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences
Seyed Hamid Rezatofighi1,2, Stephen Gould1, Ba-Ngu Vo3, Katarina Mele2, William E. Hughes4,5, and Richard Hartley1,6
1College of Engineering & Computer Sci., Australian National University, ACT, Australia
hamid.rezatofighi@anu.edu.au
2Quantitative Imaging Group, CSIRO Math., Informatics & Statistics, NSW, Australia
3Department of Electrical and Computer Engineering, Curtin University, WA, Australia
4The Garvan Institute of Medical Research, NSW, Australia
5Department of Medicine, St. Vincent’s Hospital, NSW, Australia
6National ICT (NICTA), Australia
Abstract. Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters.
LNCS 7917, p. 110 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013