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Article Dans Une Revue International Journal of Approximate Reasoning Année : 2010

Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.

Résumé

A tool called Belief Scheduler is proposed for state sequence recognition in the Transferable Belief Model (TBM) framework. This tool makes noisy temporal belief functions smoother using a Temporal Evidential Filter (TEF). The Belief Scheduler makes belief on states smoother, separates the states (assumed to be true or false) and synchronizes them in order to infer the sequence. A criterion is also provided to assess the appropriateness between observed belief functions and a given sequence model. This criterion is based on the conflict information appearing explicitly in the TBM when combining observed belief functions with predictions. The Belief Scheduler is part of a generic architecture developed for on-line and automatic human action and activity recognition in videos of athletics taken with a moving camera. In experiments, the system is assessed on a database composed of 69 real athletics video sequences. The goal is to automatically recognize running, jumping, falling and standing-up actions as well as high jump, pole vault, triple jump and {long jump activities of an athlete. A comparison with Hidden Markov Models for video classification is also provided.
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Dates et versions

hal-00475787 , version 1 (23-07-2010)

Identifiants

Citer

Emmanuel Ramasso, Costas Panagiotakis, Michèle Rombaut, Denis Pellerin. Belief Scheduler based on model failure detection in the TBM framework. Application to human activity recognition.. International Journal of Approximate Reasoning, 2010, 51 (7), pp.846-865. ⟨10.1016/j.ijar.2010.04.005⟩. ⟨hal-00475787⟩
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