Filter (signal processing)

Algorithm

Physics

Clutter

Pattern recognition (psychology)

Artificial intelligence

Kalman filter

Psychology

Pedagogy

Extended Kalman filter

Tracking (education)

State vector

Computer vision

Subspace topology

Mathematics

Computer science

Filtering problem

Probabilistic logic

Particle filter

Radar

Classical mechanics

Telecommunications

Subspace representations have been a popular way to model appearance in computer vision. In Jepson and Black's influential paper on EigenTracking, they were successfully applied in tracking. For noisy targets, optimization-based algorithms (including EigenTracking) often fail catastrophically after losing track. Particle filters have recently emerged as a robust method for tracking in the presence of multi-modal distributions. To use subspace representations in a particle filter, the number of samples increases exponentially as the state vector includes the subspace coefficients. We introduce an efficient method for using subspace representations in a particle filter by applying Rao-Blackwellization to integrate out the subspace coefficients in the state vector. Fewer samples are needed since part of the posterior over the state vector is analytically calculated. We use probabilistic principal component analysis to obtain analytically tractable integrals. We show experimental results in a scenario in which we track a target in clutter.

A Rao-Blackwellized particle filter for eigentracking