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Foreground Objects Segmentation for Moving Camera Scenarios Based on SCGMM

  • Jaime Gallego
  • Montse Pardàs
  • Montse Solano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7252)

Abstract

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor scenarios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the object.

Keywords

Object segmentation SCGMM moving camera sequences video indexation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jaime Gallego
    • 1
  • Montse Pardàs
    • 1
  • Montse Solano
    • 1
  1. 1.Department of Signal Theory and CommunicationsTechnical University of Catalonia (UPC)BarcelonaSpain

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