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1 LGI - IMAG - Laboratoire de Génie Informatique 2 TEMIS - Advanced Image Sequence Processing IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes 3 CEA-LETI - Laboratoire d-Electronique et des Technologies de l-Information

Abstract : This paper deals with the detection of moving objects. We have defined a method able to cope with perturbations frequently encountered during acquisition of outdoor image sequences: camera not perfectly stationary, illumination modifications, occlusions,

. Temporal integration and statistical regulariz- ation are the two main features of the method. A temporal multiscale decomposition allows us to detect and to characterize various dynamical behaviours of the elements present in the scene. A tracking module provides a prediction map, which gives a confidence level for presence of motion at a given pixel. A statistical regularization framework, based on Markov random field models, supplies a formal way to combine these different sets of computed information, while exploiting a priori knowledge on the primitives to be determined. A calibration technique based on so-called qualitative boxes is used to estimate model parameters. Several experiments with real image sequences depicting various complex situations have validated the approach.

keyword : MARKOV MODELS PARAMETER ESTIMATION ROBUSTNESS STATISTICAL REGULARIZATION TEMPORAL INTEGRATION MOTION DETECTION MULTIRESOLUTION IMAGE SEQUENCE





Autor: Jean-Michel Létang - Patrick Bouthemy - Véronique Rebuffel -

Fuente: https://hal.archives-ouvertes.fr/



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