Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/58138
Type: | Conference paper |
Title: | Bayesian multi-object estimation from image observations |
Author: | Vo, B. Vo, B. Suter, D. Pham, N. |
Citation: | Proceedings from the 12th International Conference on Information Fusion Seattle, WA, USA, July 6-9, 2009: pp.890-898. |
Publisher: | IEEE |
Publisher Place: | USA |
Issue Date: | 2009 |
ISBN: | 9780982443804 |
Conference Name: | International Conference on Information Fusion (12th : 2009 : Seattle, USA) |
Statement of Responsibility: | Ba-Ngu Vo, Ba-Tuong Vo, David Suter and Nam Trung Pham |
Abstract: | Analytic characterizations of the posterior distribution of a random finite set of states, conditioned on image observations are derived; under the assumption that the regions of the observation influenced by individual states do not overlap. These results provide tractable means to jointly estimate the number of states and their values in the Bayesian framework. As an application, we develop a multiobject filter suitable for image observations with low signal to noise ratio. A particle implementation of the multi-object filter is proposed and demonstrated via simulations. |
Keywords: | Random sets Multi-Bernoulli Filtering Images Tracking Track Before Detect |
Rights: | ©2009 ISIF |
Appears in Collections: | Aurora harvest Computer Science publications |
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