Multiple moving object detection for fast video content description in compressed domain

  • Authors:

  • Francesca Manerba;Jenny Benois-Pineau;Riccardo Leonardi;Boris Mansencal

  • Affiliations:

  • Department of Electronics for Automations, University of Brescia, Brescia, Italy;Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université Bordeaux 1/Bordeaux 2/CNRS/ENSEIRB, Talence Cedex, France;Department of Electronics for Automations, University of Brescia, Brescia, Italy;Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université Bordeaux 1/Bordeaux 2/CNRS/ENSEIRB, Talence Cedex, France

  • Venue:
  • EURASIP Journal on Advances in Signal Processing
  • Year:
  • 2008

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Abstract

Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content. Thinking of a video stream, different types of information can be considered semantically important. Since we can assume that the most relevant one is linked to the presence of moving foreground objects, their number, their shape, and their appearance can constitute a good mean for content description. For this reason, we propose to combine both motion information and region-based color segmentation to extract moving objects from an MPEG2 compressed video stream starting only considering low-resolution data. This approach, which we refer to as "rough indexing," consists in processing P-frame motion information first, and then in performing I-frame color segmentation. Next, since many details can be lost due to the low-resolution data, to improve the object detection results, a novel spatiotemporal filtering has been developed which is constituted by a quadric surface modeling the object trace along time. This method enables to effectively correct possible former detection errors without heavily increasing the computational effort.