On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Uniqueness of information measure in the theory of evidence
Fuzzy Sets and Systems - Special Issue: Measures of Uncertainty
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
What is Dempster-Shafer's model?
Advances in the Dempster-Shafer theory of evidence
Measures of uncertainty in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Visual perception of obstacles and vehicles for platooning
IEEE Transactions on Intelligent Transportation Systems
An obstacle detection method by fusion of radar and motion stereo
IEEE Transactions on Intelligent Transportation Systems
Object association with belief functions, an application with vehicles
Information Sciences: an International Journal
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This article describes a modification of an association algorithm for object tracking based on the evidence theory. This association algorithm was first developed by Rombaut and subsequently improved in a general way by Gruyer. This algorithm has been modified here in order to obtain better results when data reliability is poor. This article presents the basic concepts of the evidence theory. Then, the association algorithm developed by Rombaut is explained, and some examples are given to show that this algorithm fails to give the proper decision when data reliability decreases. Finally, the new algorithm is presented and the two algorithms are compared using synthetic data. In order to test the robustness of the two algorithms, they were also tested using real data coming from a CCD camera and these data can be qualified as very noisy with a reliability ranging from good to very bad.