Abstract
The Transferable Belief Model (TBM) relies on belief functions and enables one to represent and combine a variety of knowledge from certain up to ignorance as well as conflict inherent to imperfect data. A lot of applications have used this flexible framework however, in the context of temporal data analysis of belief functions, a few work have been proposed. Temporal aspect of data is essential for many applications such as surveillance (monitoring) and Human-Computer Interfaces. We propose algorithms based on the mechanisms of Hidden Markov Models usually used for state sequence analysis in probability theory. The proposed algorithms are the “credal forward”, “credal backward” and “credal Viterbi” procedures which allow to filter temporal belief functions and to assess state sequences in the TBM framework. Illustration of performance is provided on a human motion analysis problem.
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Ramasso, E., Rombaut, M., Pellerin, D. (2007). Forward-Backward-Viterbi Procedures in the Transferable Belief Model for State Sequence Analysis Using Belief Functions. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_37
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DOI: https://doi.org/10.1007/978-3-540-75256-1_37
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