dc.contributor.author | Christmas, JT | |
dc.contributor.author | Everson, RM | |
dc.contributor.author | Rodriguez-Munoz, R | |
dc.contributor.author | Tregenza, T | |
dc.date.accessioned | 2016-03-30T13:05:21Z | |
dc.date.issued | 2013 | |
dc.description.abstract | 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. | en_GB |
dc.identifier.citation | IEEE International Joint Conference on Neural Networks (IJCNN) 2013 | en_GB |
dc.identifier.doi | 10.1109/IJCNN.2013.6707130 | |
dc.identifier.uri | http://hdl.handle.net/10871/20873 | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.title | Variational Bayesian tracking: whole track convergence for large scale ecological video monitoring | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2016-03-30T13:05:21Z | |
dc.identifier.isbn | 978-1-4673-6128-6 | |
dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record. | en_GB |
dc.description | Conference paper: IEEE International Joint Conference on Neural Networks (IJCNN), 4-9 Aug. 2013, Dallas, Texas, USA. | en_GB |