Artificial Intelligence
Attribute reduction based on evidence theory in incomplete decision systems
Information Sciences: an International Journal
Improvement of an association algorithm for obstacle tracking
Information Fusion
Decision fusion for postal address recognition using belief functions
Expert Systems with Applications: An International Journal
Extending stochastic ordering to belief functions on the real line
Information Sciences: an International Journal
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Analysis of evidence-theoretic decision rules for pattern classification
Pattern Recognition
Representing uncertainty on set-valued variables using belief functions
Artificial Intelligence
On the fusion of imprecise uncertainty measures using belief structures
Information Sciences: an International Journal
Relevance and truthfulness in information correction and fusion
International Journal of Approximate Reasoning
Representing partial ignorance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Vehicle Detection and Tracking in Car Video Based on Motion Model
IEEE Transactions on Intelligent Transportation Systems
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The problem tackled in this article consists in associating perceived objects detected at a certain time with known objects previously detected, knowing uncertain and imprecise information regarding the association of each perceived objects with each known objects. For instance, this problem can occur during the association step of an obstacle tracking process, especially in the context of vehicle driving aid. A contribution in the modeling of this association problem in the belief function framework is introduced. By interpreting belief functions as weighted opinions according to the Transferable Belief Model semantics, pieces of information regarding the association of known objects and perceived objects can be expressed in a common global space of association to be combined by the conjunctive rule of combination, and a decision making process using the pignistic transformation can be made. This approach is validated on real data.