Parametric motion models for video content description within the MPEG-7 framework

Titus Zaharia; Francoise J. Preteux

doi:10.1117/12.424968

8 May 2001 Parametric motion models for video content description within the MPEG-7 framework

Titus Zaharia, Francoise J. Preteux

Proceedings Volume 4304, Nonlinear Image Processing and Pattern Analysis XII; (2001) https://doi.org/10.1117/12.424968
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

This paper addresses the issue of motion-based description of video contents within the framework of the future MPEG-7 standard. Motion is a highly relevant feature related to the spatio-temporal structure of a video and therefore should play a central role within any attempt of content-based video description. We show that parametric motion models provide a powerful tool for characterizing motion of arbitrary (foreground or background) video objects within a unified mathematical framework. The parametric object motion descriptor (POMD) principle consists of describing the motion of objects in video sequences as a 2D geometric transform. The motion models considered in this paper are those currently adopted in the future MPEG-7 standard: constant, simplified scale/rotation, affine, planar perspective and parabolic. The descriptor is defined as the vector of parameters associated with the geometric transform providing thus a very compact representation of the video object motion. Several similarity measures between motion models are considered, including simple L₁ or L₂ distances in the parameter space, and more elaborate similarity measures based upon distance functions directly related to the velocity fields. The experimental results, carried out upon the data sets created by the authors for evaluating the motion descriptors within the MPEG-7 development process, are presented and discussed.

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Titus Zaharia and Francoise J. Preteux "Parametric motion models for video content description within the MPEG-7 framework", Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, (8 May 2001); https://doi.org/10.1117/12.424968

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