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This paper presents comparative results of a model for multiple camera fusion, which is based on Dezert-Smarandache theory of evidence. Our architecture works at the decision level to track objects on a ground plane using predefined zones, producing useful information for surveillance tasks such as behavior recognition. Decisions from cameras are generated by applying a perspective-based basic belief...
The identity management problem is the problem of probabilistically keeping track of the association between target tracks and target identities, based on observations made by sensors. Updates of the belief state can happen because of new sensor observations reflecting on target identity, or because targets come near each other so that their identities become confused or mixed. Since the space of...
Principal ideas of the minC combination are recalled. A mathematical structure of generalized frames of discernment is formalized. A generalized schema for a computation of the minC combination is presented. Conflicting belief masses redistribution among non-conflicting focal elements is overviewed. Final general formulas for computation of the minC combination are presented. Some examples of computation...
In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a new probabilistic transformation, called DSmP, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the DSmP transformation works and we compare it to main existing transformations proposed in the literature...
This paper introduces an information theoretic approach to verification of causal models in modular Bayesian fusion systems. We assume distributed fusion systems which are gradually extended by adding new modules, each having a limited domain knowledge captured in local Bayesian networks. However, since different modules originate from different, independent design processes important dependencies...
In this paper we focus on targets which, in addition to reflecting signals themselves, also have a trailing path behind them, called a wake. When the detections are fed to a tracking system like the Probabilistic Data Association Filter, the estimated track can be misled and sometimes lose the real target because of the wake. This problem becomes even more severe in multitarget environments where...
To cope with asymmetric threats in an increasingly network centric environment, todaypsilas command support systems must interoperate with a diverse collection of other systems. As a natural consequence, the focus is changing from data fusion to knowledge fusion. This new reality creates the need for advanced techniques that exploit not only the syntactic structure of knowledge bases, but also the...
We present local Bayesian fusion approaches for the reduction of storage and computational costs of Bayesian fusion which is detached from fixed modelling assumptions. Using local approaches, Bayesian fusion is not performed in detail on the whole space that is spanned by the quantities of interest but only locally - at least in regions that are task relevant with a high probability. These regions...
Tracking multiple targets in a cluttered environment is a challenging task. Probabilistic multiple hypothesis tracking (PMHT) is an efficient approach for dealing with it. Essentially, PMHT is based on expectation-maximization for handling association conflicts. Linearity in the number of targets and measurements is the main motivation for a further development and extension of this methodology. However,...
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