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Multi-Target Bayes Filtering with Random Finite Sets

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Random finite sets are a natural framework for tracking an unknown number of moving targets. Explicit association between measurements and targets can be avoided by adopting a point-process formalisim. This leads to a Bayes multi-target filter with a SMC implementation. Practically this is inefficient for many targets. The Probability Hypothesis Density (PHD) approximates the multi-target posterior in the single-target space, and is more tractable to compute in an SMC implementation.

I will cover the above theory and implementation, and motivate its application to automatic polyphonic music transcription

Vo B-N., Singh S.S., and Doucet A., Sequential Monte Carlo methods for Multi-target Filtering with Random Finite Sets, IEEE Aerospace and Electronic Systems, June 2005 http://www-sigproc.eng.cam.ac.uk/%7Esss40/papers/Vo05_smcForMultiTargetFilteringWthRandomSets.pdf

This talk is part of the Audio and Music Processing (AMP) Reading Group series.

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