Variational Bayesian tracking: Whole track convergence for large-scale ecological video monitoring

Variational Bayesian approximations offer a computationally fast alternative to numerical approximations for Bayesian inference. We examine variational Bayesian methods for filtering and smoothing continuous hidden Markov models, in particular those with sharply-peaked, nonlinear observations densities. We show that, by making variational updates in the correct order, robust convergence to the tracked state may be achieved. We apply the whole track convergence algorithm to tracking wild crickets in video streams and describe how animals may be identified from the characteristics of their tracks. We also show how identifying alphanumeric tags may be read under poor lighting conditions.