Abstract:
This paper explores the use of a Bayesian inference model for updating the classifications of multiple objects simultaneously when given a measurement on only one of the ...Show MoreMetadata
Abstract:
This paper explores the use of a Bayesian inference model for updating the classifications of multiple objects simultaneously when given a measurement on only one of the objects. It is proven that with prior knowledge on the number of objects being tracked, each measurement can update the probability mass function for every tracked object. This result is generalized to sensors that can only classify subsets of the objects. The paper also shows empirically that the rate of convergence to the correct classifications for all objects using this model is improved over tracking each object independently. Finally, the paper ends by demonstrating the efficacy of the model by fusing measurements from two different classification sensors in a multiple target tracking scenario.
Published in: 14th International Conference on Information Fusion
Date of Conference: 05-08 July 2011
Date Added to IEEE Xplore: 08 August 2011
ISBN Information:
Conference Location: Chicago, IL, USA