Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/83076
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Type: Conference paper
Title: Latent data association: Bayesian model selection for multi-target tracking
Author: Segal, A.
Reid, I.
Citation: Proceedings 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 1-8 December, 2013: pp.2904-2911
Publisher: IEEE
Publisher Place: online
Issue Date: 2013
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781479928392
ISSN: 1550-5499
Conference Name: International Conference on Computer Vision (2013 : Sydney, Australia)
Statement of
Responsibility: 
Aleksandr V.Segal and Ian Reid
Abstract: We propose a novel parametrization of the data association problem for multi-target tracking. In our formulation, the number of targets is implicitly inferred together with the data association, effectively solving data association and model selection as a single inference problem. The novel formulation allows us to interpret data association and tracking as a single Switching Linear Dynamical System (SLDS). We compute an approximate posterior solution to this problem using a dynamic programming/message passing technique. This inference-based approach allows us to incorporate richer probabilistic models into the tracking system. In particular, we incorporate inference over inliers/outliers and track termination times into the system. We evaluate our approach on publicly available datasets and demonstrate results competitive with, and in some cases exceeding the state of the art. © 2013 IEEE.
Rights: © 2013 IEEE
DOI: 10.1109/ICCV.2013.361
Description (link): http://www.iccv2013.org/
http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=19984
Published version: http://dx.doi.org/10.1109/iccv.2013.361
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Computer Science publications

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