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

Authors:
E. Ramasso;C. Panagiotakis;M. Rombaut;D. Pellerin
Affiliations:
FEMTO-ST Institute, UFC/ENSMM/UTBM, Automatic Control and Micro-Mechatronic Systems Department, 25000 Besançon, France;Computer Science Department, University of Crete, P.O. Box 2208, Heraklion, Greece;GIPSA-lab, Image and Signal Department (DIS), 961 Rue de la Houille Blanche, BP46, 38402 Saint Martin d'Hères, France;GIPSA-lab, Image and Signal Department (DIS), 961 Rue de la Houille Blanche, BP46, 38402 Saint Martin d'Hères, France
Venue:
International Journal of Approximate Reasoning
Year:
2010

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Abstract

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.